技术领域Technical Field
本发明涉及电子商务技术领域，特别是一种基于用户偏好分析的商品推送方法及系统。The present invention relates to the technical field of e-commerce, and in particular to a commodity push method and system based on user preference analysis.
背景技术Background Art
在数字化和电子商务的迅猛发展背景下，个性化推荐系统已成为吸引用户并提升用户体验的关键技术，推荐系统的核心目标是准确预测用户的兴趣和偏好，以便向用户推荐最合适的商品或服务。早期的推荐系统主要依赖于简单的协同过滤技术，该技术通过分析用户的历史行为数据来预测用户可能感兴趣的新商品或服务。随着技术的进步，推荐系统逐渐融入更复杂的算法，如矩阵分解、深度学习和自然语言处理等，使得推荐结果更加精准和个性化。In the context of the rapid development of digitalization and e-commerce, personalized recommendation systems have become a key technology to attract users and improve user experience. The core goal of recommendation systems is to accurately predict users' interests and preferences in order to recommend the most suitable goods or services to users. Early recommendation systems mainly relied on simple collaborative filtering technology, which predicts new goods or services that users may be interested in by analyzing users' historical behavior data. With the advancement of technology, recommendation systems have gradually incorporated more complex algorithms, such as matrix decomposition, deep learning, and natural language processing, making recommendation results more accurate and personalized.
然而，现有的推荐技术仍存在一些不足之处，许多系统仍依赖于显式用户反馈，如评分和评论，而忽视了隐式反馈的潜在价值，未能充分利用用户情感倾向和上下文信息，导致推荐结果在某些情况下缺乏个性化和相关性，影响推荐系统的有效性和用户的最终满意度。However, existing recommendation technologies still have some shortcomings. Many systems still rely on explicit user feedback, such as ratings and comments, but ignore the potential value of implicit feedback and fail to fully utilize user sentiment tendencies and contextual information, resulting in a lack of personalization and relevance in recommendation results in some cases, affecting the effectiveness of the recommendation system and the ultimate satisfaction of users.
发明内容Summary of the invention
鉴于上述现有的基于用户偏好分析的商品推送方法及系统中存在的问题，提出了本发明。In view of the problems existing in the above-mentioned existing commodity push method and system based on user preference analysis, the present invention is proposed.
因此，本发明所要解决的问题在于许多系统仍依赖于显式用户反馈，如评分和评论，而忽视了隐式反馈的潜在价值，未能充分利用用户情感倾向和上下文信息，导致推荐结果在某些情况下缺乏个性化和相关性，影响推荐系统的有效性和用户的最终满意度。Therefore, the problem to be solved by the present invention is that many systems still rely on explicit user feedback, such as ratings and comments, but ignore the potential value of implicit feedback and fail to fully utilize user emotional tendencies and contextual information, resulting in a lack of personalization and relevance in recommendation results in some cases, affecting the effectiveness of the recommendation system and the ultimate satisfaction of users.
为解决上述技术问题，本发明提供如下技术方案：一种基于用户偏好分析的商品推送方法，其包括，收集多源数据，对用户评论进行情感倾向分析生成用户画像并构建商品知识图谱，使用模糊卡诺模型评估特征重要性并生成特征－情感对；基于用户画像和特征－情感对构建用户－商品交互图，根据用户－商品交互图中的时空信息计算商品间的上下文感知相似度；根据商品间的上下文感知相似度生成推荐列表。To solve the above technical problems, the present invention provides the following technical solutions: a product push method based on user preference analysis, which includes collecting multi-source data, performing sentiment tendency analysis on user comments to generate user portraits and constructing a product knowledge graph, using a fuzzy Kano model to evaluate feature importance and generate feature-emotion pairs; constructing a user-product interaction graph based on user portraits and feature-emotion pairs, calculating the context-aware similarity between products based on the spatiotemporal information in the user-product interaction graph; and generating a recommendation list based on the context-aware similarity between products.
作为本发明所述基于用户偏好分析的商品推送方法的一种优选方案，其中：所述收集多源数据，对用户评论进行情感倾向分析生成用户画像并构建商品知识图谱指从网站、移动应用和社交媒体收集用户的行为数据、商品数据以及上下文数据包括地理位置、时间戳以及设备信息，对收集的数据进行清洗并使用NLP工具对用户评论进行分词、去停用词和词性标注；As a preferred solution of the product push method based on user preference analysis described in the present invention, wherein: the collection of multi-source data, the sentiment analysis of user comments to generate user portraits and the construction of product knowledge graphs refers to collecting user behavior data, product data and context data including geographic location, timestamp and device information from websites, mobile applications and social media, cleaning the collected data and using NLP tools to segment user comments, remove stop words and perform part-of-speech tagging;
选择用户行为中的关键特征，包括访问频率、购买频次和社交互动频率，使用one-hot编码和TF-IDF方法将行为特征转换为向量表示并识别评论中的特征词，将行为数据与地理位置、时间戳和设备信息整合，为每个用户行为记录附上上下文标签；Select key features of user behavior, including visit frequency, purchase frequency, and social interaction frequency. Use one-hot encoding and TF-IDF methods to convert behavior features into vector representations and identify feature words in comments. Integrate behavior data with geographic location, timestamp, and device information, and attach context labels to each user behavior record.
使用BERT模型进行情感分析，将特征词输入BERT模型得到文本的特征向量，将BERT模型输出的特征向量输入由全连接层和Softmax激活函数构成分类层，输出每个情感类别的概率；Use the BERT model for sentiment analysis. Input the feature words into the BERT model to obtain the feature vector of the text. Input the feature vector output by the BERT model into the classification layer composed of a fully connected layer and a Softmax activation function to output the probability of each sentiment category.
在另一个全连接层使用线性函数计算情感强度：In another fully connected layer, a linear function is used to calculate the sentiment intensity:
式中，I是情感强度，σ是Sigmoid函数，Vi和bi是情感强度层的权重和偏差，T是转置操作；Where I is the sentiment intensity, σ is the Sigmoid function,Vi andbi are the weights and biases of the sentiment intensity layer, and T is the transposition operation;
根据情感类别概率值和情感强度为每条评论计算情感得分：Calculate the sentiment score for each comment based on the sentiment category probability value and sentiment intensity:
Sl＝P(rl)×I(rl)，Sl =P(rl )×I(rl ),
式中Sl是第l条用户评论的情感得分，P(rl)是第l条用户评论的情感情感类别概率值，I(rl)是第l条用户评论的情感强度；Where Sl is the sentiment score of the lth user comment, P(rl ) is the sentiment category probability value of the lth user comment, and I(rl ) is the sentiment intensity of the lth user comment;
计算每个用户的总体情感得分，综合所有评论的情感信息：Calculate the overall sentiment score for each user, combining the sentiment information of all comments:
式中，Stotal是用户的总体情感得分，N是用户的评论数量；Where Stotal is the user's overall sentiment score, and N is the number of comments by the user;
根据总情感得分为用户分配情感标签：Assign sentiment labels to users based on their total sentiment scores:
若Stotal≥0.75，则情感标签为“积极”；If Stotal ≥ 0.75, the sentiment label is “positive”;
若0.75>Stotal>0.25，则情感标签为“中立”；If 0.75>Stotal >0.25, the sentiment label is “neutral”;
若Stotal≤0.25，则情感标签为“消极”；If Stotal ≤ 0.25, the sentiment label is “negative”;
将行为特征向量与情感得分结合形成用户画像并使用K-means聚类算法将用户划分为不同的群体；Combine the behavioral feature vector with the sentiment score to form a user portrait and use the K-means clustering algorithm to divide users into different groups;
从收集的商品数据中提取特征进行标准化后将属性信息转换为特征向量A，使用one-hot编码表示类别和品牌，标准化价格和评分；Extract features from the collected product data, standardize them, and convert the attribute information into feature vector A. Use one-hot encoding to represent categories and brands, and standardize prices and ratings.
通过共现分析和协同过滤技术识别商品之间的关系，使用点互信息计算商品之间的关系强度PMI(x,y)：The relationship between products is identified through co-occurrence analysis and collaborative filtering technology, and the relationship strength PMI (x, y) between products is calculated using point mutual information:
式中，P(x,y)是商品x和y同时出现的概率，P(x)和P(y)分别为商品x和商品y单独出现的概率；Where P(x,y) is the probability of goods x and y appearing at the same time, P(x) and P(y) are the probabilities of goods x and y appearing separately, respectively;
为每个商品创建一个节点，节点属性包括商品特征向量A和用户情感标签，根据PMI值和用户的行为模式定义商品间的边缘；Create a node for each product. The node attributes include the product feature vector A and the user sentiment label. Define the edges between products based on the PMI value and the user's behavior pattern.
当有新商品加入以及商品信息发生变化时进行更新，重新计算相关商品的PMI值，并在商品知识图谱中更新相应的边缘权重和节点信息。When new products are added or product information changes, updates are performed, the PMI values of related products are recalculated, and the corresponding edge weights and node information are updated in the product knowledge graph.
作为本发明所述基于用户偏好分析的商品推送方法的一种优选方案，其中：所述使用模糊卡诺模型评估特征重要性并生成特征－情感对指设计包含提取的商品特征的问卷，使用Likert量表询问用户对每个商品特征的满意度和期望级别，使用在线调查工具收集用户响应，对用户响应进行编码转换，将Likert量表分数转化为模糊集合所需的数值输入；As a preferred solution of the product push method based on user preference analysis of the present invention, wherein: the use of the fuzzy Kano model to evaluate feature importance and generate feature-emotion pairs refers to designing a questionnaire containing the extracted product features, using a Likert scale to ask users about their satisfaction and expectation level for each product feature, using an online survey tool to collect user responses, encoding and converting user responses, and converting Likert scale scores into numerical inputs required by fuzzy sets;
对于满意度和期望级别，定义三个模糊集合，包括高模糊集合、中模糊集合和低模糊集合，使用三角隶属函数表示每个模糊集合：For the satisfaction and expectation levels, three fuzzy sets are defined, including a high fuzzy set, a medium fuzzy set, and a low fuzzy set, and each fuzzy set is represented using a triangular membership function:
式中，δ(q)是隶属函数，a'，b'，c'是针对高、中、低满意度的分界点，q是评分值；In the formula, δ(q) is the membership function, a', b', c' are the cut-off points for high, medium and low satisfaction, and q is the score value;
使用模糊卡诺逻辑，将特征根据用户满意度和期望级别的模糊集合隶属度进行分类，使用模糊AND计算每个商品特征的重要性指数：Using fuzzy Kano logic, the features are classified according to the fuzzy set membership of user satisfaction and expectation level, and the importance index of each product feature is calculated using fuzzy AND:
Gt＝min(μsat(f),μexp(f))，Gt =min(μsat (f), μexp (f)),
式中，Gt是商品特征t的重要性指数，μsat(f)和μexp(f)分别是满意度和期望的模糊隶属度；In the formula,Gt is the importance index of product feature t, μsat (f) and μexp (f) are the fuzzy membership of satisfaction and expectation respectively;
根据特征重要性指数和情感得分生成每个商品特征的综合重要性报告；Generate a comprehensive importance report for each product feature based on the feature importance index and sentiment score;
结合特征的情感得分和重要性指数为每个商品生成一个特征－情感对列表，每个特征－情感对包括特征名称、情感得分和重要性指数。The sentiment score and importance index of the feature are combined to generate a feature-sentiment pair list for each product. Each feature-sentiment pair includes the feature name, sentiment score and importance index.
作为本发明所述基于用户偏好分析的商品推送方法的一种优选方案，其中：所述基于用户画像和特征－情感对构建用户－商品交互图指将用户的行为数据与特征－情感对结合，对每一层用户与商品的交互，标注相关的商品特征及用户对商品特征的情感得分和重要性指数；As a preferred solution of the product push method based on user preference analysis of the present invention, wherein: the construction of the user-product interaction graph based on the user portrait and feature-emotion pair refers to combining the user's behavior data with the feature-emotion pair, marking the relevant product features and the user's emotional score and importance index of the product features for each layer of user-product interaction;
对每个独立的用户创建一个节点，节点属性包括用户的标识信息，对每个独立的商品创建一个节点，节点属性包括商品的ID、类别以及品牌，将用户的每组行为映射为用户－商品交互图的边缘并将上下文信息作为边缘属性，使用图数据库存储用户－商品交互图。A node is created for each independent user, and the node attributes include the user's identification information. A node is created for each independent product, and the node attributes include the product ID, category, and brand. Each set of user behaviors is mapped to the edge of the user-product interaction graph and the context information is used as the edge attribute. The user-product interaction graph is stored in a graph database.
作为本发明所述基于用户偏好分析的商品推送方法的一种优选方案，其中：所述根据用户－商品交互图中的时空信息计算商品间的上下文感知相似度指使用GraphSAGE模型从交互图中学习商品的嵌入表示，嵌入向量生成公式为：As a preferred solution of the product push method based on user preference analysis of the present invention, the calculation of the context-aware similarity between products based on the spatiotemporal information in the user-product interaction graph refers to learning the embedded representation of the product from the interaction graph using the GraphSAGE model, and the embedding vector generation formula is:
式中，是第k层中节点v的嵌入向量，是节点u在第k-1层的嵌入向量，Q(v)是节点v的邻居节点集合，A是聚合函数，W(k)是权重矩阵，B(k)是第k层的偏置项，μ为ReLU激活函数；In the formula, is the embedding vector of node v in layer k, is the embedding vector of node u at the k-1th layer, Q(v) is the set of neighbor nodes of node v, A is the aggregation function, W(k) is the weight matrix, B(k) is the bias term of the kth layer, and μ is the ReLU activation function;
结合商品的嵌入向量和上下文属性，计算商品间的上下文感知相似度：Combining the embedding vectors and contextual attributes of the items, we can calculate the context-aware similarity between the items:
式中，Simog是商品o和商品g的相似度，ho和hg是商品的嵌入向量，γ是上下文的权重因子，O(o,g)是上下文相似度；Where Simog is the similarity between product o and product g, ho and hg are the embedding vectors of the products, γ is the weight factor of the context, and O(o,g) is the context similarity;
根据计算得到的相似度，为用户推荐上下文相关的相似商品。Based on the calculated similarity, similar context-related products are recommended to users.
作为本发明所述基于用户偏好分析的商品推送方法的一种优选方案，其中：所述根据商品间的上下文感知相似度生成推荐列表指对于每个用户，从与用户历史交互商品相似度最高的商品中选取M个商品，作为推荐候选集，根据用户当前的上下文过滤不符合当前情境的商品，优化候选集；As a preferred solution of the product push method based on user preference analysis of the present invention, wherein: the generating of the recommendation list according to the context-aware similarity between products means that for each user, M products are selected from the products with the highest similarity to the user's historical interaction products as the recommendation candidate set, and products that do not meet the current context are filtered according to the user's current context to optimize the candidate set;
为每个候选商品构建特征向量，使用GBDT模型对每个用户的推荐候选集进行得分预测，根据预测得分对推荐候选集进行递减排序，根据排序结果选择顶部M个商品形成最终的推荐列表，按照推荐列表向用户推送用户感兴趣的商品内容。Construct a feature vector for each candidate product, use the GBDT model to predict the score of each user's recommendation candidate set, sort the recommendation candidate set in descending order according to the predicted score, select the top M products based on the sorting results to form the final recommendation list, and push the product content that the user is interested in to the user according to the recommendation list.
作为本发明所述基于用户偏好分析的商品推送方法的一种优选方案，其中：所述按照推荐列表向用户推送用户感兴趣的商品内容指定义推送频率和时间根据用户在线状态和活跃时间进行即时推送，实时监控和记录用户对推送商品的反应，将收集的用户反馈整合到用户画像中，实时更新用户偏好和行为模式。As a preferred solution of the product push method based on user preference analysis described in the present invention, wherein: pushing the product content that the user is interested in to the user according to the recommendation list refers to defining the push frequency and time to perform instant push according to the user's online status and active time, real-time monitoring and recording of user reactions to the pushed products, integrating the collected user feedback into the user portrait, and updating user preferences and behavior patterns in real time.
本发明的另外一个目的是提供一种基于用户偏好分析的商品推送系统，其包括，Another object of the present invention is to provide a product push system based on user preference analysis, which comprises:
数据收集模块，用于从多个渠道收集用户行为数据、商品数据和上下文数据并对数据进行预处理；A data collection module, which is used to collect user behavior data, product data, and context data from multiple channels and pre-process the data;
构建模块，用于从预处理数据中提取关键特征构建用户画像并基于商品数据的分析构建商品知识图谱；A construction module is used to extract key features from preprocessed data to build user portraits and to build a product knowledge graph based on the analysis of product data;
评估模块，用于计算每个商品特征的重要性指数并结合商品特征的情感得分和重要性指数生成特征－情感对；Evaluation module, which is used to calculate the importance index of each product feature and generate feature-sentiment pairs by combining the sentiment score and importance index of the product feature;
分析模块，用于根据用户画像和特征－情感对构建用户－商品交互图并使用GraphSAGE模型从交互图中学习商品的嵌入表示，并计算商品间的上下文感知相似度；The analysis module is used to construct a user-item interaction graph based on user profiles and feature-sentiment pairs, learn the embedded representation of items from the interaction graph using the GraphSAGE model, and calculate the context-aware similarity between items;
推荐模块，用于根据相似度生成初步推荐列表并使用GBDT模型对列表进行个性化排序最终生成个性化推荐列表，根据列表推送商品并收集用户反馈。The recommendation module is used to generate a preliminary recommendation list based on similarity and use the GBDT model to personalize the sorting of the list to finally generate a personalized recommendation list, push products based on the list and collect user feedback.
一种计算机设备，包括：存储器和处理器；所述存储器存储有计算机程序，所述处理器执行所述计算机程序时实现基于用户偏好分析的商品推送方法的步骤。A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of a commodity push method based on user preference analysis when executing the computer program.
一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现基于用户偏好分析的商品推送方法的步骤。A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a commodity push method based on user preference analysis.
本发明有益效果为：本发明通过使用情感分析技术和模糊卡诺模型，不仅提高了用户画像的精确度，还通过特征－情感对精细化了商品特征的重要性评估，构建用户－商品交互图并结合上下文感知相似度计算，不仅考虑了用户的实时位置和环境变化，还能动态调整推荐内容，显著提高了推荐系统的个性化程度和用户满意度。The beneficial effects of the present invention are as follows: the present invention not only improves the accuracy of user portraits by using sentiment analysis technology and fuzzy Kano model, but also refines the importance evaluation of product features through feature-sentiment pairs, constructs a user-product interaction graph and combines it with context-aware similarity calculation, which not only takes into account the user's real-time location and environmental changes, but also dynamically adjusts the recommended content, significantly improving the personalization of the recommendation system and user satisfaction.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
为了更清楚地说明本发明实施例的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.
图1为基于用户偏好分析的商品推送方法的流程示意图。FIG. 1 is a flow chart of a method for pushing products based on user preference analysis.
图2为特征－情感对生成的实施示意图。Figure 2 is a schematic diagram of the implementation of feature-sentiment pair generation.
图3为基于用户偏好分析的商品推送系统的结构示意图。FIG. 3 is a schematic diagram of the structure of a product push system based on user preference analysis.
具体实施方式DETAILED DESCRIPTION
为使本发明的上述目的、特征和优点能够更加明显易懂，下面结合说明书附图对本发明的具体实施方式作详细地说明。In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.
在下面的描述中阐述了很多具体细节以便于充分理解本发明，但是本发明还可以采用其他不同于在此描述的其他方式来实施，本领域技术人员可以在不违背本发明内涵的情况下做类似推广，因此本发明不受下面公开的具体实施例的限制。In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.
其次，此处所称的“一个实施例”或“实施例”是指可包含于本发明至少一个实现方式中的特定特征、结构或特性。在本说明书中不同地方出现的“在一个实施例中”并非均指同一个实施例，也不是单独的或选择性地与其他实施例互相排斥的实施例。Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.
实施例1，参照图1和图2，为本发明第一个实施例，该实施例提供了一种基于用户偏好分析的商品推送方法，基于用户偏好分析的商品推送方法包括，Embodiment 1, referring to FIG. 1 and FIG. 2, is the first embodiment of the present invention. This embodiment provides a commodity push method based on user preference analysis. The commodity push method based on user preference analysis includes:
S1、收集多源数据，对用户评论进行情感倾向分析生成用户画像并构建商品知识图谱，使用模糊卡诺模型评估特征重要性并生成特征－情感对；S1. Collect multi-source data, analyze user comments for sentiment tendency to generate user portraits and build product knowledge graphs, use fuzzy Kano model to evaluate feature importance and generate feature-sentiment pairs;
具体的，收集多源数据，对用户评论进行情感倾向分析生成用户画像并构建商品知识图谱指从网站、移动应用和社交媒体收集用户的行为数据、商品数据以及上下文数据包括地理位置、时间戳以及设备信息，对收集的数据进行清洗并使用NLP工具对用户评论进行分词、去停用词和词性标注；Specifically, collecting multi-source data, analyzing user comments for sentiment tendency to generate user portraits and constructing product knowledge graphs means collecting user behavior data, product data and contextual data including geographic location, timestamp and device information from websites, mobile applications and social media, cleaning the collected data and using NLP tools to segment user comments, remove stop words and perform part-of-speech tagging;
选择用户行为中的关键特征，包括访问频率、购买频次和社交互动频率，使用one-hot编码和TF-IDF方法将行为特征转换为向量表示并识别评论中的特征词包括电池寿命，屏幕分辨率；Select key features of user behavior, including visit frequency, purchase frequency, and social interaction frequency, use one-hot encoding and TF-IDF methods to convert behavioral features into vector representations and identify characteristic words in reviews, including battery life and screen resolution;
将行为数据与地理位置、时间戳和设备信息整合，为每个用户行为记录附上上下文标签；Integrate behavioral data with geolocation, timestamp, and device information to attach contextual tags to each user behavior record;
使用BERT模型进行情感分析，将特征词输入BERT模型得到文本的特征向量，将BERT模型输出的特征向量输入由全连接层和Softmax激活函数构成分类层，输出每个情感类别的概率：Use the BERT model for sentiment analysis, input the feature words into the BERT model to obtain the feature vector of the text, input the feature vector output by the BERT model into the classification layer composed of the fully connected layer and the Softmax activation function, and output the probability of each sentiment category:
式中，P(y＝c|z)是给定特征向量z时，预测为类别c的概率，Wc和bc是类别c的权重和偏差，C是情感类别的总数，Wj是类别j相关的权重矩阵，bj是类别j相关的偏差，T是转置操作，权重和偏差的计算是通过初始化、损失函数的定义、反向传播和优化算法的迭代应用实现的；Where P(y=c|z) is the probability of predicting category c given feature vector z,Wc andbc are the weight and bias of category c, C is the total number of sentiment categories,Wj is the weight matrix associated with category j,bj is the bias associated with category j, T is the transpose operation, and the calculation of weights and biases is achieved through initialization, definition of loss function, backpropagation, and iterative application of optimization algorithm;
在另一个全连接层使用线性函数计算情感强度：In another fully connected layer, a linear function is used to calculate the sentiment intensity:
式中，I是情感强度，取值范围为[0,1]，σ是Sigmoid函数，Vi和bi是情感强度层的权重和偏差，T是转置操作；Where I is the sentiment intensity, ranging from [0, 1], σ is the Sigmoid function,Vi andbi are the weights and biases of the sentiment intensity layer, and T is the transposition operation;
根据情感类别概率值和情感强度为每条评论计算情感得分：Calculate the sentiment score for each comment based on the sentiment category probability value and sentiment intensity:
Sl＝P(rl)×I(rl)，Sl =P(rl )×I(rl ),
式中Sl是第l条用户评论的情感得分，P(rl)是第l条用户评论的情感类别概率值，I(rl)是第l条用户评论的情感强度(0到1之间的实数)；Where Sl is the sentiment score of the lth user comment, P(rl ) is the sentiment category probability value of the lth user comment, and I(rl ) is the sentiment intensity of the lth user comment (a real number between 0 and 1);
计算每个用户的总体情感得分，综合所有评论的情感信息：Calculate the overall sentiment score for each user, combining the sentiment information of all comments:
式中，Stotal是用户的总体情感得分，范围为[0,1]，N是用户的评论数量；Where Stotal is the user's overall sentiment score, ranging from [0,1], and N is the number of comments by the user;
根据总情感得分为用户分配情感标签：Assign sentiment labels to users based on their total sentiment scores:
若Stotal≥0.75，则情感标签为“积极”；If Stotal ≥ 0.75, the sentiment label is “positive”;
若0.75>Stotal>0.25，则情感标签为“中立”；If 0.75>Stotal >0.25, the sentiment label is “neutral”;
若Stotal≤0.25，则情感标签为“消极”；If Stotal ≤ 0.25, the sentiment label is “negative”;
选择0.75和0.25作为阈值是基于四分位数的思想，其中数据被分为四等分，每部分包含25％的数据点，积极情感通常认为是得分较高的评论，因此选择高于75百分位数的数据点作为积极情感可以确保只有真正积极的评论被分类为积极，消极情感的阈值设定在25百分位以下，确保被分类为消极的评论确实包含较多的消极情绪，这种基于数据的四分位数方法可以适应不同的数据集和情感分布，使得情感分类更加贴近实际的数据分布，这样的划分可以较好地平衡各类别之间的数量，避免某一类别过多或过少，从而提高模型的泛化能力和准确性；The selection of 0.75 and 0.25 as thresholds is based on the idea of quartiles, where the data is divided into four equal parts, each containing 25% of the data points. Positive sentiment is usually considered to be a comment with a higher score, so selecting data points above the 75th percentile as positive sentiment can ensure that only truly positive comments are classified as positive. The threshold for negative sentiment is set below the 25th percentile to ensure that comments classified as negative do contain more negative emotions. This data-based quartile method can adapt to different data sets and sentiment distributions, making sentiment classification closer to the actual data distribution. Such a division can better balance the number of categories and avoid too many or too few categories, thereby improving the generalization ability and accuracy of the model;
将行为特征向量与情感得分结合形成用户画像并使用K-means聚类算法将用户划分为不同的群体；Combine the behavioral feature vector with the sentiment score to form a user portrait and use the K-means clustering algorithm to divide users into different groups;
从收集的商品数据中提取特征包括类别、品牌、价格和评分，进行标准化后将属性信息转换为特征向量A，使用one-hot编码表示类别和品牌，标准化价格和评分；Extract features including category, brand, price and rating from the collected product data, convert the attribute information into feature vector A after standardization, use one-hot encoding to represent category and brand, and standardize price and rating;
通过共现分析和协同过滤技术识别商品之间的关系，使用点互信息计算商品之间的关系强度PMI(x,y)：The relationship between products is identified through co-occurrence analysis and collaborative filtering technology, and the relationship strength PMI (x, y) between products is calculated using point mutual information:
式中，P(x,y)是商品x和y同时出现的概率，P(x)和P(y)分别为商品x和商品y单独出现的概率；Where P(x,y) is the probability of goods x and y appearing at the same time, P(x) and P(y) are the probabilities of goods x and y appearing separately, respectively;
为每个商品创建一个节点，节点属性包括商品特征向量A和用户情感标签，根据PMI值和用户的行为模式定义商品间的边缘，边缘属性反映商品间的关系类型和强度；Create a node for each product. The node attributes include the product feature vector A and the user sentiment label. Define the edges between products based on the PMI value and the user's behavior pattern. The edge attributes reflect the type and strength of the relationship between products.
当有新商品加入以及商品信息发生变化时进行更新，重新计算相关商品的PMI值，并在商品知识图谱中更新相应的边缘权重和节点信息。When new products are added or product information changes, updates are performed, the PMI values of related products are recalculated, and the corresponding edge weights and node information are updated in the product knowledge graph.
通过从网站、移动应用和社交媒体等多渠道收集用户行为数据和上下文信息，平台可以获得丰富且多样化的数据集，反映用户的真实兴趣和行为模式。利用NLP工具对用户评论进行情感倾向分析，BERT模型能准确捕捉用户评论中的复杂情感，当用户在平台上浏览商品并留下评论时，系统能够实时分析其情感倾向。如果用户在评价中表达了对某类商品(如电子产品)强烈的正面情感，系统将自动调整用户画像，记录该类商品的偏好，为后续的商品推荐提供支持，通过将用户的行为特征(如访问频率、购买频次)与情感分析结果相结合，系统构建了精确的用户画像。同时，商品知识图谱实时更新商品关系和用户偏好变化，确保推荐系统的及时性，用户画像的准确性直接影响商品知识图谱的构建，而实时更新的知识图谱又反过来优化用户画像，使得两者之间形成正向反馈，当用户不断浏览不同类别的商品(如电子设备、家居用品)，系统会根据浏览频率和评论中的情感倾向实时更新用户画像，识别用户对电子设备的特殊偏好，并在知识图谱中加强电子设备节点与用户节点的关系权重，增强后续推荐的精准度，利用K-means聚类算法对用户进行群体划分，根据情感得分和行为特征将用户分为不同的兴趣群体。通过这种方式，推荐系统可以识别并推送更符合用户偏好的个性化内容，显著提高推荐的相关性和准确性。By collecting user behavior data and contextual information from multiple channels such as websites, mobile applications, and social media, the platform can obtain rich and diverse data sets that reflect users' real interests and behavior patterns. Using NLP tools to analyze the sentiment tendency of user comments, the BERT model can accurately capture the complex emotions in user comments. When users browse products on the platform and leave comments, the system can analyze their sentiment tendencies in real time. If a user expresses strong positive emotions towards a certain type of product (such as electronic products) in the evaluation, the system will automatically adjust the user portrait, record the preference for this type of product, and provide support for subsequent product recommendations. By combining the user's behavioral characteristics (such as visit frequency, purchase frequency) with the results of sentiment analysis, the system builds an accurate user portrait. At the same time, the product knowledge graph updates product relationships and user preference changes in real time to ensure the timeliness of the recommendation system. The accuracy of the user portrait directly affects the construction of the product knowledge graph, and the real-time updated knowledge graph in turn optimizes the user portrait, so that a positive feedback is formed between the two. When users continue to browse different categories of products (such as electronic devices, household items), the system will update the user portrait in real time based on the browsing frequency and the emotional tendency in the comments, identify the user's special preference for electronic devices, and strengthen the relationship weight between the electronic device node and the user node in the knowledge graph, enhance the accuracy of subsequent recommendations, and use the K-means clustering algorithm to divide users into groups, and divide users into different interest groups based on emotional scores and behavioral characteristics. In this way, the recommendation system can identify and push personalized content that is more in line with user preferences, significantly improving the relevance and accuracy of recommendations.
用户群体划分提供了更细致的用户群像，这使得推荐系统能够在不同群体间进行差异化推荐，从而提高推荐的成功率，平台可以识别出喜欢科技产品的用户群体，向他们推荐最新的科技产品及相关配件，或者为喜爱家居产品的用户推送家居用品促销信息。群体划分使得每个用户都能收到符合其兴趣的商品推荐，从而提升用户的购买意图和平台的销售额，商品知识图谱的动态更新机制使得系统能够灵活应对市场变化和用户需求波动。实时数据更新确保系统的推荐结果始终基于最新的信息，为企业提供有力的市场决策支持，动态适应性确保了推荐系统的实时性，而实时数据又为市场决策提供了精准的依据，当新品上架或某商品销量激增时，系统会自动更新商品知识图谱中相关节点和边缘的权重，调整推荐策略，优先推荐新品和热门商品。企业可以利用这些信息调整库存、制定促销策略，提高市场响应速度和盈利能力。User group segmentation provides a more detailed user group image, which enables the recommendation system to make differentiated recommendations between different groups, thereby improving the success rate of recommendations. The platform can identify user groups that like technology products and recommend the latest technology products and related accessories to them, or push home furnishing promotion information to users who like home products. Group segmentation allows each user to receive product recommendations that match their interests, thereby increasing the user's purchase intention and the platform's sales. The dynamic update mechanism of the product knowledge graph enables the system to flexibly respond to market changes and user demand fluctuations. Real-time data updates ensure that the system's recommendation results are always based on the latest information, providing strong market decision-making support for enterprises. Dynamic adaptability ensures the real-time nature of the recommendation system, and real-time data provides an accurate basis for market decisions. When new products are launched or the sales of a certain product surge, the system will automatically update the weights of relevant nodes and edges in the product knowledge graph, adjust the recommendation strategy, and give priority to recommending new products and popular products. Enterprises can use this information to adjust inventory, formulate promotion strategies, and improve market response speed and profitability.
进一步地，使用模糊卡诺模型评估特征重要性并生成特征－情感对指设计包含提取的商品特征的问卷，使用Likert量表(1—5分)询问用户对每个商品特征的满意度和期望级别，使用在线调查工具收集用户响应，对用户响应进行编码转换，将Likert量表分数转化为模糊集合所需的数值输入；Furthermore, the fuzzy Kano model is used to evaluate the importance of features and generate feature-sentiment pairs. A questionnaire containing the extracted product features is designed. A Likert scale (1-5 points) is used to ask users about their satisfaction and expectation level for each product feature. An online survey tool is used to collect user responses. User responses are coded and converted to convert the Likert scale scores into the numerical inputs required by the fuzzy set.
对于满意度和期望级别，定义三个模糊集合，包括高模糊集合、中模糊集合和低模糊集合，使用三角隶属函数表示每个模糊集合：For the satisfaction and expectation levels, three fuzzy sets are defined, including a high fuzzy set, a medium fuzzy set, and a low fuzzy set, and each fuzzy set is represented using a triangular membership function:
式中，δ(q)是隶属函数，取值范围为[0,1]，a'，b'，c'是针对高、中、低满意度的分界点，q是评分值；In the formula, δ(q) is the membership function, with a value range of [0,1], a', b', c' are the cutoff points for high, medium, and low satisfaction, and q is the score value;
使用模糊卡诺逻辑，将特征根据用户满意度和期望级别的模糊集合隶属度进行分类，使用模糊AND计算每个商品特征的重要性指数：Using fuzzy Kano logic, the features are classified according to the fuzzy set membership of user satisfaction and expectation level, and the importance index of each product feature is calculated using fuzzy AND:
Gt＝min(μsat(f),μexp(f))；Gt =min(μsat (f), μexp (f));
式中，Gt是商品特征t的重要性指数，μsat(f)和μexp(f)分别是满意度和期望的模糊隶属度；In the formula,Gt is the importance index of product feature t, μsat (f) and μexp (f) are the fuzzy membership of satisfaction and expectation respectively;
根据特征重要性指数和情感得分生成每个商品特征的综合重要性报告；Generate a comprehensive importance report for each product feature based on the feature importance index and sentiment score;
结合特征的情感得分和重要性指数为每个商品生成一个特征－情感对列表，每个特征－情感对包括特征名称、情感得分和重要性指数。The sentiment score and importance index of the feature are combined to generate a feature-sentiment pair list for each product. Each feature-sentiment pair includes the feature name, sentiment score and importance index.
通过设计包含提取商品特征的问卷，并使用Likert量表收集用户满意度和期望级别，模糊卡诺模型能够细致地评估每个商品特征的重要性。这一过程结合了用户的主观感知和客观数据分析，生成了更为全面的特征重要性指数，使用模糊集合和三角隶属函数表示用户满意度和期望，将模糊逻辑引入特征评估中，显著提高了特征重要性的辨识度，隶属函数通过分段线性函数计算出不同满意度下的模糊隶属度，精准捕捉用户对特征的模糊认知。这种分析不仅能够识别出用户认为至关重要的特征，还能够根据用户的反馈调整商品开发和改进方向。例如，若“电池寿命”在满意度和期望的模糊隶属度中都表现出高重要性指数，则该特征应成为商品改进的重点，结合特征的情感得分和重要性指数，生成特征－情感对，进一步深化了对用户偏好的理解。每个特征－情感对包括特征名称、情感得分和重要性指数，提供了用户情感与特征偏好的详细概览，计算的特征重要性指数Gt综合了用户的满意度和期望，确保每个商品特征的重要性都能被量化和比较。这一结果与情感得分结合，生成的特征－情感对列表为每个商品提供了精准的情感和性能分析，通过特征－情感对的分析，系统能够生成每个商品特征的综合重要性报告。这一报告不仅为企业的市场策略提供了有力支持，还为产品优化提供了明确的方向。例如，通过分析某款智能手机的特征－情感对，可以发现用户对“屏幕分辨率”的积极情感和高重要性评估，从而建议在市场推广中强调这一特性。By designing a questionnaire that extracts product features and using a Likert scale to collect user satisfaction and expectation levels, the fuzzy Kano model can carefully evaluate the importance of each product feature. This process combines the user's subjective perception and objective data analysis to generate a more comprehensive feature importance index. Fuzzy sets and triangular membership functions are used to represent user satisfaction and expectations. Fuzzy logic is introduced into feature evaluation, which significantly improves the recognition of feature importance. The membership function calculates the fuzzy membership under different satisfaction levels through a piecewise linear function, accurately capturing the user's fuzzy cognition of the feature. This analysis can not only identify the features that users consider to be crucial, but also adjust the direction of product development and improvement based on user feedback. For example, if "battery life" shows a high importance index in both the fuzzy membership of satisfaction and expectation, then this feature should be the focus of product improvement. Combining the feature's sentiment score and importance index, feature-sentiment pairs are generated, further deepening the understanding of user preferences. Each feature-sentiment pair includes the feature name, sentiment score, and importance index, providing a detailed overview of user sentiment and feature preferences. The calculated feature importance indexGt integrates user satisfaction and expectations, ensuring that the importance of each product feature can be quantified and compared. This result is combined with the sentiment score to generate a list of feature-sentiment pairs that provides accurate sentiment and performance analysis for each product. Through the analysis of feature-sentiment pairs, the system can generate a comprehensive importance report for each product feature. This report not only provides strong support for the company's market strategy, but also provides a clear direction for product optimization. For example, by analyzing the feature-sentiment pairs of a certain smartphone, it can be found that users have positive emotions and high importance assessments for "screen resolution", thus suggesting that this feature should be emphasized in marketing promotion.
S2、基于用户画像和特征－情感对构建用户－商品交互图，根据用户－商品交互图中的时空信息计算商品间的上下文感知相似度；S2, construct a user-product interaction graph based on user portraits and feature-emotion pairs, and calculate the context-aware similarity between products based on the spatiotemporal information in the user-product interaction graph;
具体的，基于用户画像和特征－情感对构建用户－商品交互图指将用户的行为数据与特征－情感对结合，为每条用户行为数据标注相关的商品特征和用户情感，对每一层用户与商品的交互，标注相关的商品特征及用户对商品特征的情感得分和重要性指数；Specifically, constructing a user-product interaction graph based on user portraits and feature-emotion pairs means combining user behavior data with feature-emotion pairs, annotating relevant product features and user emotions for each piece of user behavior data, and annotating relevant product features and the user's emotional score and importance index for each layer of user-product interaction;
对每个独立的用户创建一个节点，节点属性包括用户的标识信息，对每个独立的商品创建一个节点，节点属性包括商品的ID、类别以及品牌，将用户的每组行为映射为用户－商品交互图的边缘并将上下文信息作为边缘属性，使用图数据库存储用户－商品交互图。A node is created for each independent user, and the node attributes include the user's identification information. A node is created for each independent product, and the node attributes include the product ID, category, and brand. Each set of user behaviors is mapped to the edge of the user-product interaction graph and the context information is used as the edge attribute. The user-product interaction graph is stored in a graph database.
为每个独立的用户创建一个节点，节点属性包括用户的标识信息、行为特征、兴趣标签等，这些属性能够全面描绘用户的个人资料和行为模式，为每个独立的商品创建一个节点，节点属性包括商品的ID、类别、品牌以及通过特征－情感对获取的情感得分和特征重要性，这些信息帮助系统识别商品在用户视角中的价值，通过用户和商品节点的创建，系统能够精准地标识和描述每一个用户和商品。这种详细的节点信息为后续的数据分析和推荐奠定了坚实的基础，商品节点包含的特征－情感对信息使推荐系统能够考虑用户的情感倾向和特征偏好，从而提高推荐的个性化和准确性，通过将用户行为转化为图中的边缘，系统能够全面捕捉用户与商品之间的复杂交互关系。这种多维度的交互建模有助于揭示用户的深层需求和潜在兴趣，通过将用户行为转化为图中的边缘，系统能够全面捕捉用户与商品之间的复杂交互关系。这种多维度的交互建模有助于揭示用户的深层需求和潜在兴趣，利用用户和商品节点及其间的边缘关系，构建完整的用户－商品交互图。该图反映了用户与商品之间的所有交互关系，包括直接互动和间接关联。A node is created for each independent user. The node attributes include the user's identification information, behavioral characteristics, interest tags, etc. These attributes can fully describe the user's profile and behavior pattern. A node is created for each independent product. The node attributes include the product's ID, category, brand, and the sentiment score and feature importance obtained through the feature-sentiment pair. This information helps the system identify the value of the product from the user's perspective. Through the creation of user and product nodes, the system can accurately identify and describe each user and product. This detailed node information lays a solid foundation for subsequent data analysis and recommendation. The feature-sentiment pair information contained in the product node enables the recommendation system to consider the user's emotional tendency and feature preference, thereby improving the personalization and accuracy of the recommendation. By converting user behavior into edges in the graph, the system can fully capture the complex interactive relationship between users and products. This multi-dimensional interaction modeling helps to reveal the user's deep needs and potential interests. By converting user behavior into edges in the graph, the system can fully capture the complex interactive relationship between users and products. This multi-dimensional interaction modeling helps to reveal the user's deep needs and potential interests. A complete user-product interaction graph is constructed using user and product nodes and the edge relationships between them. The graph reflects all interactive relationships between users and products, including direct interactions and indirect associations.
进一步地，根据用户－商品交互图中的时空信息计算商品间的上下文感知相似度指使用GraphSAGE模型从交互图中学习商品的嵌入表示，嵌入向量生成公式为：Furthermore, calculating the context-aware similarity between products based on the spatiotemporal information in the user-product interaction graph refers to using the GraphSAGE model to learn the embedded representation of products from the interaction graph. The embedding vector generation formula is:
式中，是第k层中节点v的嵌入向量，是节点u在第k-1层的嵌入向量，Q(v)是节点v的邻居节点集合，A是聚合函数，常见的包括均值、最大值、求和等，本实施例中优选为均值聚合，W(k)是权重矩阵，B(k)是第k层的偏置项，μ为ReLU激活函数；In the formula, is the embedding vector of node v in layer k, is the embedding vector of node u at the k-1th layer, Q(v) is the set of neighbor nodes of node v, A is an aggregation function, common ones include mean, maximum, sum, etc., in this embodiment, mean aggregation is preferred, W(k) is the weight matrix, B(k) is the bias term of the kth layer, and μ is the ReLU activation function;
结合商品的嵌入向量和上下文属性，计算商品间的上下文感知相似度：Combining the embedding vectors and contextual attributes of the items, we can calculate the context-aware similarity between the items:
式中，Simog是商品o和商品g的相似度，ho和hg是商品的嵌入向量，γ是上下文的权重因子，通过确定影响用户购买决策上下文因素(如时间、地点、设备类型等)，使用统计分析方法(如方差分析、卡方检验)评估不同上下文因素对用户行为的影响程度，确定各上下文因素的影响力，为权重因子γ设定提供依据，基于分析结果，设定初步的γ值，例如，如果发现时间因素对用户购买行为影响显著，可以相应地设置较高的γ值，设计A/B测试或多变量测试，将用户随机分配到不同的实验组，每组应用不同的γ值，比较不同γ值下的推荐系统性能，关注指标包括点击率、转化率、用户满意度等，使用机器学习模型的验证误差、信息检索的精确度和召回率等指标来评估不同γ值的效果，根据实验结果调整γ的值，寻找最优解，选取在实验中表现最好的γ值作为最终值，C(o,g)是上下文相似度；In the formula, Simog is the similarity between product o and product g, ho and hg are the embedding vectors of the products, γ is the context weight factor. By determining the context factors that affect user purchase decisions (such as time, location, device type, etc.), statistical analysis methods (such as variance analysis, chi-square test) are used to evaluate the degree of influence of different context factors on user behavior, determine the influence of each context factor, and provide a basis for setting the weight factor γ. Based on the analysis results, a preliminary γ value is set. For example, if it is found that the time factor has a significant impact on user purchase behavior, a higher γ value can be set accordingly. An A/B test or multivariate test is designed to randomly assign users to different experimental groups, each group using a different γ value, and compare the performance of the recommendation system under different γ values. The indicators of interest include click-through rate, conversion rate, user satisfaction, etc. The verification error of the machine learning model, the precision and recall rate of information retrieval, and other indicators are used to evaluate the effects of different γ values. The value of γ is adjusted according to the experimental results to find the optimal solution. The γ value that performs best in the experiment is selected as the final value. C(o,g) is the context similarity;
根据计算得到的相似度，为用户推荐上下文相关的相似商品。Based on the calculated similarity, similar context-related products are recommended to users.
GraphSAGE模型通过逐层采样和聚合邻居节点信息，生成的嵌入向量能够捕捉商品间的复杂关系和特征。这种高维嵌入表示为相似度计算提供了精准的数据支持，提升了模型的表征能力，GraphSAGE利用采样技术，避免了传统图神经网络中全图计算的复杂性，提高了大规模数据集的计算效率。这一特性使得系统能够处理大量用户和商品数据，保持高效的实时响应，嵌入向量不仅用于商品相似度的计算，还为个性化推荐系统提供基础，直接影响最终的推荐精度，上下文感知相似度结合商品嵌入和上下文属性，能够根据用户的实时需求和偏好进行动态调整，确保推荐的商品高度相关。这种个性化推荐提升了用户满意度和参与度，通过结合上下文信息(如用户的时间偏好、地理位置)，推荐系统能够识别并匹配用户的具体需求，提供更具针对性的产品建议，增加购买意向，上下文感知相似度的计算依赖于实时的上下文信息，这与交互图的动态更新和GraphSAGE模型的嵌入表示学习形成闭环，使得系统能够快速响应并调整推荐策略。The GraphSAGE model generates an embedding vector that can capture the complex relationships and features between products by sampling and aggregating neighbor node information layer by layer. This high-dimensional embedding representation provides accurate data support for similarity calculation and improves the representation ability of the model. GraphSAGE uses sampling technology to avoid the complexity of full-graph calculation in traditional graph neural networks and improves the computational efficiency of large-scale data sets. This feature enables the system to process a large amount of user and product data and maintain efficient real-time response. The embedding vector is not only used for the calculation of product similarity, but also provides a basis for the personalized recommendation system, which directly affects the final recommendation accuracy. Context-aware similarity combines product embedding and context attributes, and can be dynamically adjusted according to the user's real-time needs and preferences to ensure that the recommended products are highly relevant. This personalized recommendation improves user satisfaction and engagement. By combining contextual information (such as user's time preference and geographic location), the recommendation system can identify and match the user's specific needs, provide more targeted product recommendations, and increase purchase intention. The calculation of context-aware similarity depends on real-time context information, which forms a closed loop with the dynamic update of the interaction graph and the embedding representation learning of the GraphSAGE model, enabling the system to respond quickly and adjust the recommendation strategy.
S3、根据商品间的上下文感知相似度生成推荐列表；S3, generating a recommendation list based on the context-aware similarity between products;
具体的，根据商品间的上下文感知相似度生成推荐列表指对于每个用户，从与用户历史交互商品相似度最高的商品中选取N个商品，作为推荐候选集，根据用户当前的上下文过滤不符合当前情境的商品，优化候选集；Specifically, generating a recommendation list based on the context-aware similarity between products means that for each user, N products are selected from the products with the highest similarity to the user's historical interaction products as the recommendation candidate set, and products that do not meet the current context are filtered out according to the user's current context to optimize the candidate set;
为每个候选商品构建特征向量，包括商品相似度、用户历史行为、上下文信息、商品属性和情感分析得分，使用GBDT模型进行排序预测，使用历史用户交互数据训练GBDT模型，应用交叉验证和网格搜索技术优化模型参数，定义优化目标为最大化点击率和用户满意度；Construct a feature vector for each candidate product, including product similarity, user historical behavior, contextual information, product attributes, and sentiment analysis scores. Use the GBDT model for ranking prediction. Use historical user interaction data to train the GBDT model. Apply cross-validation and grid search techniques to optimize model parameters. Define the optimization goal as maximizing click-through rate and user satisfaction.
使用训练好的GBDT模型对每个用户的推荐候选集进行得分预测，根据预测得分对推荐候选集进行递减排序，根据排序结果选择顶部N个商品形成最终的推荐列表；Use the trained GBDT model to predict the score of each user's recommendation candidate set, sort the recommendation candidate set in descending order according to the predicted score, and select the top N products according to the sorting results to form the final recommendation list;
按照推荐列表向用户推送用户感兴趣的商品内容。Push product content that the user is interested in to the user according to the recommendation list.
通过分析用户的实时上下文信息(如地理位置、浏览时间、设备类型等)，为用户提供与当前情境高度相关的商品推荐。这种上下文感知能力使得推荐不仅基于用户的历史行为，而且响应其即时的环境和情绪变化，极大增强了推荐的相关性和吸引力，通过提供时空相关的商品推荐，系统能够显著提升用户的购物体验，减少信息过载，增加用户对推荐系统的信任和满意度。例如，用户在假日前夕搜索礼物时，系统能够推荐符合节日气氛的商品，增强购买意愿，通过使用交叉验证和网格搜索技术，本发明确保GBDT模型在不同数据子集上表现的一致性和优越性，系统性地找到最优模型参数。这种方法提高了模型的泛化能力，降低了过拟合的风险，GBDT模型的优化使得处理用户请求的速度更快，提高了系统对实时数据的响应能力，使用户几乎感觉不到等待，从而提升了用户的整体满意度，根据用户的当前情境动态调整候选集，确保推荐的商品不仅与用户的个人偏好相符，而且适应其当前的需求。这种灵活的候选集调整策略提高了推荐的接受率和点击率，利用GBDT模型对候选商品进行个性化排序，考虑了商品的多维度特征，如相似度、用户历史行为评分和上下文信息，使得推荐结果更加精准和有针对性。通过分析推荐效果和用户反馈，本发明能够为企业提供关于用户偏好和行为趋势的洞察，辅助企业在市场推广、存货管理和新产品开发等方面做出更有信息支持的决策，系统能够实时捕捉和分析市场动态，响应用户行为的变化，帮助企业快速调整市场策略，把握市场机遇。By analyzing the user's real-time context information (such as geographic location, browsing time, device type, etc.), the user is provided with product recommendations that are highly relevant to the current situation. This context-aware capability makes the recommendation not only based on the user's historical behavior, but also responds to its immediate environmental and emotional changes, greatly enhancing the relevance and attractiveness of the recommendation. By providing time-space related product recommendations, the system can significantly improve the user's shopping experience, reduce information overload, and increase the user's trust and satisfaction with the recommendation system. For example, when a user searches for gifts on the eve of a holiday, the system can recommend products that match the festive atmosphere and enhance the willingness to buy. By using cross-validation and grid search techniques, the present invention ensures the consistency and superiority of the GBDT model on different data subsets and systematically finds the optimal model parameters. This method improves the generalization ability of the model and reduces the risk of overfitting. The optimization of the GBDT model makes it faster to process user requests, improves the system's responsiveness to real-time data, and makes the user feel almost no waiting, thereby improving the user's overall satisfaction. The candidate set is dynamically adjusted according to the user's current situation to ensure that the recommended products are not only consistent with the user's personal preferences, but also adapt to their current needs. This flexible candidate set adjustment strategy improves the acceptance rate and click-through rate of recommendations. It uses the GBDT model to personalize the sorting of candidate products, taking into account the multi-dimensional characteristics of products, such as similarity, user historical behavior scores, and contextual information, making the recommendation results more accurate and targeted. By analyzing the recommendation effect and user feedback, the present invention can provide companies with insights into user preferences and behavior trends, assisting companies in making more informed decisions in marketing, inventory management, and new product development. The system can capture and analyze market dynamics in real time, respond to changes in user behavior, and help companies quickly adjust market strategies and seize market opportunities.
进一步地，按照推荐列表向用户推送用户感兴趣的商品内容指定义推送频率和时间根据用户在线状态和活跃时间进行即时推送，实时监控和记录用户对推送商品的反应，包括点击率、购买转化率和页面停留时间，并提供用户反馈通道，将收集的用户反馈整合到用户画像中，实时更新用户偏好和行为模式。Furthermore, pushing product content that the user is interested in to the user according to the recommendation list means defining the push frequency and time to perform instant push according to the user's online status and active time, monitoring and recording the user's response to the pushed products in real time, including click-through rate, purchase conversion rate and page dwell time, and providing a user feedback channel, integrating the collected user feedback into the user portrait, and updating the user preferences and behavior patterns in real time.
通过准确的在线状态检测，能够在用户最活跃的时间进行推送，提高推送内容的曝光率和点击率，推送频率的动态调整，使推送节奏更符合用户习惯，避免过度打扰用户，提升用户体验，系统实时记录用户在接收推送内容后的行为，包括点击率、购买转化率和页面停留时间。利用数据分析技术，评估推送内容的效果，提供用户反馈通道，用户可以通过该通道提交对推送内容的意见和建议，帮助系统了解用户的真实需求和满意度，通过持续整合用户反馈，系统能够保持用户画像的实时更新，使得用户画像更加贴合真实的用户行为。Through accurate online status detection, push content can be sent when users are most active, increasing the exposure and click-through rate of push content. Dynamic adjustment of push frequency makes the push rhythm more in line with user habits, avoids excessive disturbance of users, and improves user experience. The system records the user's behavior after receiving push content in real time, including click-through rate, purchase conversion rate, and page dwell time. Using data analysis technology, the effect of push content is evaluated, and a user feedback channel is provided. Users can submit opinions and suggestions on push content through this channel to help the system understand the real needs and satisfaction of users. By continuously integrating user feedback, the system can keep the user portrait updated in real time, making the user portrait more in line with real user behavior.
实施例2，参照图3，为本发明第二个实施例，该实施例不同于上一个实施例，提供了一种基于用户偏好分析的商品推送系统，其包括，Embodiment 2, referring to FIG. 3 , is a second embodiment of the present invention. This embodiment is different from the previous embodiment and provides a product push system based on user preference analysis, which includes:
数据收集模块，用于从多个渠道收集用户行为数据、商品数据和上下文数据并对数据进行预处理；A data collection module, which is used to collect user behavior data, product data, and context data from multiple channels and pre-process the data;
构建模块，用于从预处理数据中提取关键特征构建用户画像并基于商品数据的分析构建商品知识图谱；A construction module is used to extract key features from preprocessed data to build user portraits and to build a product knowledge graph based on the analysis of product data;
评估模块，用于计算每个商品特征的重要性指数并结合商品特征的情感得分和重要性指数生成特征－情感对；Evaluation module, which is used to calculate the importance index of each product feature and generate feature-sentiment pairs by combining the sentiment score and importance index of the product feature;
分析模块，用于根据用户画像和特征－情感对构建用户－商品交互图并使用GraphSAGE模型从交互图中学习商品的嵌入表示，并计算商品间的上下文感知相似度；The analysis module is used to construct a user-item interaction graph based on user profiles and feature-sentiment pairs, learn the embedded representation of items from the interaction graph using the GraphSAGE model, and calculate the context-aware similarity between items;
推荐模块，用于根据相似度生成初步推荐列表并使用GBDT模型对列表进行个性化排序最终生成个性化推荐列表，根据列表推送商品并收集用户反馈。The recommendation module is used to generate a preliminary recommendation list based on similarity and use the GBDT model to personalize the sorting of the list to finally generate a personalized recommendation list, push products based on the list and collect user feedback.
所述功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.
在流程图中表示或在此以其他方式描述的逻辑和/或步骤，例如，可以被认为是用于实现逻辑功能的可执行指令的定序列表，可以具体实现在任何计算机可读介质中，以供指令执行系统、装置或设备(如基于计算机的系统、包括处理器的系统或其他可以从指令执行系统、装置或设备取指令并执行指令的系统)使用，或结合这些指令执行系统、装置或设备而使用。就本说明书而言，“计算机可读介质”可以是任何可以包含、存储、通信、传播或传输程序以供指令执行系统、装置或设备或结合这些指令执行系统、装置或设备而使用的装置。The logic and/or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.
计算机可读介质的更具体的示例(非穷尽性列表)包括以下：具有一个或多个布线的电连接部(电子装置)、便携式计算机盘盒(磁装置)、随机存取存储器(RAM)、只读存储器(ROM)、可擦除可编辑只读存储器(EPROM或闪速存储器)、光纤装置以及便携式光盘只读存储器(CDROM)。另外，计算机可读介质甚至可以是可在其上打印所述程序的纸或其他合适的介质，因为可以例如通过对纸或其他介质进行光学扫描，接着进行编辑、解译或必要时以其他合适方式进行处理来以电子方式获得所述程序，然后将其存储在计算机存储器中。More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.
应当理解，本发明的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方案中，多个步骤或方法可以用存储在存储器中且由合适的指令执行系统执行的软件或固件来实现。例如，如果用硬件来实现，和在另一实施方案中一样，可用本领域公知的下列技术中的任一项或他们的组合来实现：具有用于对数据信号实现逻辑功能的逻辑门电路的离散逻辑电路，具有合适的组合逻辑门电路的专用集成电路，可编程门阵列(PGA)，现场可编程门阵列(FPGA)等。It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119537704A (en)* | 2025-01-22 | 2025-02-28 | 山东科技大学 | A time-aware user portrait modeling method based on sentiment analysis |
| CN120125294B (en)* | 2025-05-09 | 2025-08-12 | 厦门众联世纪股份有限公司 | Personalized content recommendation and ROI (region of interest) lifting method and system based on multidimensional user portraits |
| CN120125324B (en)* | 2025-05-13 | 2025-08-19 | 浙江开心果数智科技有限公司 | Commodity recommendation method and system based on big data analysis |
| CN120355474B (en)* | 2025-06-18 | 2025-10-03 | 北京资采信息技术有限公司 | A user portrait characterization method and system based on big data |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114564956A (en)* | 2022-02-28 | 2022-05-31 | 中国石油大学(华东) | A Sentiment Mining Model of User Comment Text for Automatic Extraction of Fine-grained Attributes |
| CN116579824A (en)* | 2023-05-18 | 2023-08-11 | 山西大学 | A method and system for recommending products on an e-commerce network incorporating review information |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110543242B (en)* | 2019-07-25 | 2023-07-04 | 北京智慧章鱼科技有限公司 | Expression input method and device based on BERT technology |
| KR20210047229A (en)* | 2019-10-21 | 2021-04-29 | 배재대학교 산학협력단 | Recommendation System and METHOD Reflecting Purchase Criteria and Product Reviews Sentiment Analysis |
| CN115170216A (en)* | 2022-01-17 | 2022-10-11 | 昆明理工大学 | A Product Recommendation Method Based on Knowledge Graph Incorporating Comment Sentiment and Rating |
| US12361455B2 (en)* | 2022-06-15 | 2025-07-15 | At&T Intellectual Property 1, L.P. | Method and system for personalizing metaverse object recommendations or reviews |
| CN115618125A (en)* | 2022-09-29 | 2023-01-17 | 山东师范大学 | A multi-behavior recommendation method and system based on multi-image semantic fusion |
| CN117573844B (en)* | 2024-01-15 | 2024-04-05 | 深圳市加推科技有限公司 | Data recommendation method and device based on context awareness and related medium |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114564956A (en)* | 2022-02-28 | 2022-05-31 | 中国石油大学(华东) | A Sentiment Mining Model of User Comment Text for Automatic Extraction of Fine-grained Attributes |
| CN116579824A (en)* | 2023-05-18 | 2023-08-11 | 山西大学 | A method and system for recommending products on an e-commerce network incorporating review information |
| Publication number | Publication date |
|---|---|
| CN118941365A (en) | 2024-11-12 |
| Publication | Publication Date | Title |
|---|---|---|
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