CN112541111A - Commodity retrieval and commodity recommendation system based on decentralized big data retrieval market - Google Patents
Commodity retrieval and commodity recommendation system based on decentralized big data retrieval marketDownload PDFInfo
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Abstract
Description
Technical Field
The invention belongs to the technical field of electronic commerce, and particularly relates to a commodity retrieval and commodity recommendation system based on a decentralized big data retrieval market.
Background
At present, the information quantity is continuously increased and the social production capacity is improved at any time, the types and the quantity of commodities provided by merchants are increased day by day, and in the face of the ocean of commodity information, a consumer is difficult to quickly and effectively select the commodities required by the consumer, how to arouse the desire of the consumer in browsing, how to continuously recommend the valuable commodities after the consumer chooses necessary commodities, how to improve the loyalty of the consumer to the merchants and the like are in front of the merchants. In this context, a product recommendation system, also called a personalized recommendation system, is developed to recommend objects meeting the user's requirements according to the user's characteristics, such as hobbies and interests.
The existing personalized commodity recommendation system has the following defects: 1. the demands of merchants are ignored, and the real intelligent commodity recommendation system not only needs to consider the personal demands of customers, but also needs to consider the demands of merchants, such as certain marketable commodities, and can sell commodities quickly even if advertisement propaganda is not carried out;
2. the method has the advantages that massive data cannot be processed, large merchants have tens of thousands of commodities, transactions for tens of thousands of times per day accumulate massive transaction data, the transaction data greatly help to provide intelligent commodity recommendation, however, how to process the massive data stream is a very difficult problem, if the method cannot be solved, the response time of a recommendation system is slow, the system cannot be used practically, or the method can only be used for small merchants, but cannot be used for large department stores and supermarket chains.
In summary, the problems of the prior art are as follows: the existing commodity recommendation system cannot give consideration to the requirements of users and commodity service providers, cannot guarantee the paid use of user information, and cannot process massive data.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a commodity retrieval and commodity recommendation system based on a decentralized big data retrieval market.
The invention is realized in this way, a commodity retrieval and commodity recommendation system based on decentralized big data retrieval market, the commodity retrieval and commodity recommendation system based on decentralized big data retrieval market specifically includes:
an information retrieval module: the retrieval behavior of the user for acquiring the retrieval data and generating a user behavior analysis report;
a user data acquisition module: the behavior data of the user used for collecting the uploaded information;
big data retrieval market: the device comprises an information storage unit and an information matching unit; the system is used for storing the collected related user data and performing accurate matching and pushing of information.
Further, the big data retrieval market specifically includes:
an information storage unit: the system is used for storing the collected related user behavior data;
an information matching unit: the system is used for pushing user requirements to a commodity service provider by adopting a big data technology based on the collected related user behavior data, and simultaneously, issuing income after data is used and pushing commodities to target customers to users uploading information.
Another object of the present invention is to provide a method for retrieving and recommending commodities based on a decentralized big data search market, which is applied to the system for retrieving and recommending commodities based on a decentralized big data search market, the method for retrieving and recommending commodities based on a decentralized big data search market specifically comprising:
the method comprises the steps of firstly, collecting a user retrieval behavior of retrieval data, and collecting user related behavior data of uploaded information;
step two, storing and retrieving related data, retrieving and matching information based on a big data analysis technology, generating a big data analysis report, and returning the report to a user retrieving data;
and step three, providing general requirements or customized requirements related data of the user about the goods or services to the goods service provider based on the big data analysis result, and meanwhile, accurately pushing related goods and service information to the target customer.
Further, the commodity retrieval and commodity recommendation method based on the decentralized big data retrieval market further comprises the following steps:
and after the user-related behavior data of the uploaded information is stored and used for behavior analysis, the big data retrieval market settles the income of the use of the related data for the user.
Further, in the second step, the big data analysis-based technology comprises:
step 1: constructing a big data analysis model by using the characteristics and retrieval behaviors of the current retrieval user, and constructing an original big data analysis model by using the characteristics and historical retrieval behaviors of other users and purchase records;
step 2: introducing fuzzy clustering, clustering by the system according to the characteristics of the original big data analysis model, gathering the original big data analysis model with high user characteristics and interest similarity degree into the same class and identifying, and establishing a high-similarity original big data analysis model library;
and step 3: calculating the degree of satisfaction of the attribute of the original big data analysis model to be searched and the attribute index requirement of the big data analysis model by using the similarity retrieval of the user original big data analysis model, and finding out an original big data analysis model set of which the big data analysis model is more than a certain similarity level alpha;
and 4, step 4: the retrieved original big data analysis model belongs to which original big data analysis model class; if the target raw big data analysis model belongs to a plurality of classes, the class with the minimum average distance is the class to which the target raw big data analysis model belongs;
and 5: recommending the original big data analysis model by using a classical fuzzy average clustering algorithm;
step 6: if matching the similarity value JmixIf the new big data analysis model result is larger than a given threshold and the new big data analysis model result is not affiliated to any cluster subset under the classification threshold, adding the new big data analysis model result and the original big data analysis model into an original big data analysis model base to automatically complete the learning of the original big data analysis model;
and 7: after the user inputs the intermediate storage system, the user identity prompts the user to pass, and after the user inputs the data format, the storage interface is called and a commodity information storage transaction instruction is sent to the storage interface; wherein the data format comprises a general part and a secret part;
and 8: after receiving a transaction saving instruction, acquiring a public account registered on a commodity recommendation system, logging in the commodity recommendation system, and sending a saving instruction to the commodity recommendation system after passing verification;
further, the method for establishing the original big data analysis model comprises the following steps:
establishing an original big data analysis model system of a quadruple (U, A, V, F); wherein U is { U ═1，u2，...，unThe method is characterized in that the method is a set of original big data analysis model entities, and is called a discourse domain; the method comprises the following steps that an original big data analysis model space A is equal to C and U, C is a condition attribute set, and D is a decision set; is a set of global attribute values, VaIs the value range of attribute a; f is a function of UxA → V. The domain of discourse can be divided according to the condition attributes, and the objects in the domain of discourse are divided into decision classes with different decision attributes according to the difference of the condition attributes.
Further, the method for establishing the original big data analysis model further comprises the following steps:
the characteristics and the historical retrieval behaviors of the user are attributed to a condition attribute set, the historical purchase records of the user are attributed to a decision set, and any one of the original mature original big data analysis models is represented by m characteristic indexes, namely C ═ a1，a2，...，am}，D＝{f1(r1，r2，...，rD)，f2(r1，r2，...，rp)，...fq(r1，r2，...，rp) In which aiIs the condition attribute of the historical user characteristic and the retrieval behavior, fi is the historical purchase record, belongs to the decision set or the recommendation set, riIs the relevant object attribute of the purchase record;
further, in step 8, after the commodity recommendation system receives the storage instruction, calling a transaction interface, creating a transaction record in the commodity recommendation system, and creating a special transaction value at the same time; the commodity recommending system starts an internal public account B to automatically create a transaction according to the special transaction value, and the value ensures the successful transaction, so that data is written into the commodity recommending system, and a transaction unique ID is sent to the information storage unit;
the information storage unit stores and backs up the transaction unique ID, corresponds to the ID of the commodity and stores a special transaction value, and then sends the transaction unique ID and the ID of the commodity to the display module;
the display module displays the unique ID of the transaction and the ID of the commodity;
when a user inputs a transaction unique ID or a commodity ID, the information retrieval module obtains a special transaction value in the information storage unit, then a history inquiry interface is called, the special transaction value is sent to a commodity recommendation system, the commodity recommendation system inquires history data, and the commodity recommendation system data is sent to the display unit; the display unit displays the inquired commodity information in the commodity recommendation system to the user.
Further, after the third step, the following steps are also required: and after the user-related behavior data of the uploaded information is stored and used for behavior analysis, the big data retrieval market settles the income of the use of the related data for the user.
It is a further object of the invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:
collecting user retrieval behaviors of the retrieval data, and collecting user related behavior data of the uploaded information;
storing and retrieving related data, performing retrieval matching of information based on a big data analysis technology, generating a big data analysis report, and returning the report to a user retrieving data;
based on the big data analysis result, general demand or customized demand related data of the user about the goods or services are provided for the goods service provider, and meanwhile related goods and service information is accurately pushed to the target customer.
It is another object of the present invention to provide a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
collecting user retrieval behaviors of the retrieval data, and collecting user related behavior data of the uploaded information;
storing and retrieving related data, performing retrieval matching of information based on a big data analysis technology, generating a big data analysis report, and returning the report to a user retrieving data;
based on the big data analysis result, general demand or customized demand related data of the user about the goods or services are provided for the goods service provider, and meanwhile related goods and service information is accurately pushed to the target customer.
In summary, the advantages and positive effects of the invention are: the invention provides a set of visual system which helps a user to efficiently submit personal relevant information about shopping (color preference, purchase record, inquiry record, browsing information, personalized customization demand and the like) to a decentralized big data retrieval market and assists the user in pricing data use right; the system can help the user to efficiently retrieve the data in the decentralized big data retrieval market and generate a big data analysis report; meanwhile, the system can help the commodity and service provider to accurately push the commodity and service information to the target customer after the big data analysis.
The system of the present invention may also assist the user in accurately pushing the general or customized needs of a good or service to a good or service provider.
The method can maximally reduce the search space of the nearest neighbor case according to the case search space, reduce the matching calculation time, enhance the real-time performance of personalized recommendation, and simultaneously can truly and objectively reflect the search preference of the user, thereby greatly improving the personalized degree of the user.
Drawings
Fig. 1 is a schematic structural diagram of a commodity retrieval and commodity recommendation system based on a decentralized big data retrieval market according to an embodiment of the present invention.
In the figure: 1. an information retrieval module; 2. a user data acquisition module; 3. big data retrieval market; 4. an information storage unit; 5. and an information matching unit.
Fig. 2 is a schematic diagram of a commodity retrieval and commodity recommendation system based on a decentralized big data retrieval market according to an embodiment of the present invention.
Fig. 3 is a flowchart of a commodity retrieval and commodity recommendation method based on a decentralized big data retrieval market according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The technical scheme and the technical effect of the invention are explained in detail in the following with the accompanying drawings.
As shown in fig. 1-2, the commodity retrieval and commodity recommendation system based on the decentralized big data retrieval market according to the embodiment of the present invention specifically includes:
information retrieval module 1: the system is used for collecting retrieval behaviors of users retrieving data and generating user behavior analysis reports.
The user data acquisition module 2: the behavior data of the user who uploads the information is collected.
Big data retrieval market 3: comprises aninformation storage unit 4 and aninformation matching unit 5; the system is used for storing the collected related user data and performing accurate matching and pushing of information.
The bigdata retrieval market 3 provided by the embodiment of the invention specifically comprises:
information storage unit 4: for storing the collected relevant user behavior data.
The information matching unit 5: the system is used for pushing user requirements to a commodity service provider by adopting a big data technology based on the collected related user behavior data, and simultaneously, issuing income after data is used and pushing commodities to target customers to users uploading information.
As shown in fig. 3, the method for commodity retrieval and commodity recommendation based on a decentralized big data retrieval market according to an embodiment of the present invention specifically includes:
s101, collecting the user retrieval behavior of the retrieval data and collecting the user related behavior data of the uploaded information.
And S102, storing and retrieving related data, performing retrieval matching on information based on a big data analysis technology, generating a big data analysis report, and returning the report to a user for retrieving data.
S103, providing general requirements or customized requirements related data of the user about the goods or services to the goods service provider based on the big data analysis result, and meanwhile accurately pushing related goods and service information to the target customer.
In step S102, the big data analysis-based technique includes:
step 1: constructing a big data analysis model by using the characteristics and retrieval behaviors of the current retrieval user, and constructing an original big data analysis model by using the characteristics and historical retrieval behaviors of other users and purchase records;
step 2: introducing fuzzy clustering, clustering by the system according to the characteristics of the original big data analysis model, gathering the original big data analysis model with high user characteristics and interest similarity degree into the same class and identifying, and establishing a high-similarity original big data analysis model library;
and step 3: calculating the degree of satisfaction of the attribute of the original big data analysis model to be searched and the attribute index requirement of the big data analysis model by using the similarity retrieval of the user original big data analysis model, and finding out an original big data analysis model set of which the big data analysis model is more than a certain similarity level alpha;
and 4, step 4: the retrieved original big data analysis model belongs to which original big data analysis model class; if the target raw big data analysis model belongs to a plurality of classes, the class with the minimum average distance is the class to which the target raw big data analysis model belongs;
and 5: recommending the original big data analysis model by using a classical fuzzy average clustering algorithm;
step 6: if matching the similarity value JmixIf the new big data analysis model result is larger than a given threshold and the new big data analysis model result is not affiliated to any cluster subset under the classification threshold, adding the new big data analysis model result and the original big data analysis model into an original big data analysis model base to automatically complete the learning of the original big data analysis model;
and 7: after the user inputs the intermediate storage system, the user identity prompts the user to pass, and after the user inputs the data format, the storage interface is called and a commodity information storage transaction instruction is sent to the storage interface; wherein the data format comprises a general part and a secret part;
and 8: after receiving a transaction saving instruction, acquiring a public account registered on a commodity recommendation system, logging in the commodity recommendation system, and sending a saving instruction to the commodity recommendation system after passing verification;
the method for establishing the original big data analysis model comprises the following steps:
establishing an original big data analysis model system of a quadruple (U, A, V, F); wherein U is { U ═1，u2，...，unThe method is characterized in that the method is a set of original big data analysis model entities, and is called a discourse domain; the method comprises the following steps that an original big data analysis model space A is equal to C and U, C is a condition attribute set, and D is a decision set; is a set of global attribute values, VaIs the value range of attribute a; f is a function of UxA → V. The domain of discourse can be divided according to the condition attributes, and the objects in the domain of discourse are divided into decision classes with different decision attributes according to the difference of the condition attributes.
The method for establishing the original big data analysis model further comprises the following steps:
the characteristics and the historical retrieval behaviors of the user are attributed to a condition attribute set, the historical purchase records of the user are attributed to a decision set, and any one of the original mature original big data analysis models is represented by m characteristic indexes, namely C ═ a1，a2，...，am}，D＝{f1(r1，r2，...，rD)，f2(r1，r2，...，rp)，...fq(r1，r2，...，rp) In which aiIs the condition attribute of the historical user characteristic and the retrieval behavior, fi is the historical purchase record, belongs to the decision set or the recommendation set, riIs the relevant object attribute of the purchase record;
step 8, after the commodity recommendation system receives the storage instruction, calling a transaction interface, creating a transaction record in the commodity recommendation system, and creating a special transaction value at the same time; the commodity recommending system starts an internal public account B to automatically create a transaction according to the special transaction value, and the value ensures the successful transaction, so that data is written into the commodity recommending system, and a transaction unique ID is sent to the information storage unit;
the information storage unit stores and backs up the transaction unique ID, corresponds to the ID of the commodity and stores a special transaction value, and then sends the transaction unique ID and the ID of the commodity to the display module;
the display module displays the unique ID of the transaction and the ID of the commodity;
when a user inputs a transaction unique ID or a commodity ID, the information retrieval module obtains a special transaction value in the information storage unit, then a history inquiry interface is called, the special transaction value is sent to a commodity recommendation system, the commodity recommendation system inquires history data, and the commodity recommendation system data is sent to the display unit; the display unit displays the inquired commodity information in the commodity recommendation system to the user;
after step S103, the following steps are also performed: and after the user-related behavior data of the uploaded information is stored and used for behavior analysis, the big data retrieval market settles the income of the use of the related data for the user.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A commodity retrieval and commodity recommendation system based on a decentralized big data retrieval market is characterized by specifically comprising:
an information retrieval module: the retrieval behavior of the user for acquiring the retrieval data and generating a user behavior analysis report;
a user data acquisition module: the behavior data of the user used for collecting the uploaded information;
big data retrieval market: the device comprises an information storage unit and an information matching unit; the system is used for storing the collected related user data and performing accurate matching and pushing of information.
2. The system for commodity retrieval and commodity recommendation based on a decentralized big data retrieval market of claim 1, wherein said big data retrieval market specifically comprises:
an information storage unit: the system is used for storing the collected related user behavior data;
an information matching unit: the system is used for pushing user requirements to a commodity service provider by adopting a big data technology based on the collected related user behavior data, and simultaneously, issuing income after data is used and pushing commodities to target customers to users uploading information.
3. The method for retrieving and recommending commodities based on the decentralized big data search market, applied to the system for retrieving and recommending commodities based on the decentralized big data search market as claimed in claim 1, wherein the method for retrieving and recommending commodities based on the decentralized big data search market specifically comprises:
the method comprises the steps of firstly, collecting a user retrieval behavior of retrieval data, and collecting user related behavior data of uploaded information;
step two, storing and retrieving related data, retrieving and matching information based on a big data analysis technology, generating a big data analysis report, and returning the report to a user retrieving data;
and step three, providing general requirements or customized requirements related data of the user about the goods or services to the goods service provider based on the big data analysis result, and meanwhile, accurately pushing related goods and service information to the target customer.
4. The method for commodity retrieval and commodity recommendation based on decentralized big data retrieval market of claim 3, wherein in step two, the big data analysis based technology comprises:
step 1: constructing a big data analysis model by using the characteristics and retrieval behaviors of the current retrieval user, and constructing an original big data analysis model by using the characteristics and historical retrieval behaviors of other users and purchase records;
step 2: introducing fuzzy clustering, clustering by the system according to the characteristics of the original big data analysis model, gathering the original big data analysis model with high user characteristics and interest similarity degree into the same class and identifying, and establishing a high-similarity original big data analysis model library;
and step 3: calculating the degree of satisfaction of the attribute of the original big data analysis model to be searched and the attribute index requirement of the big data analysis model by using the similarity retrieval of the user original big data analysis model, and finding out an original big data analysis model set of which the big data analysis model is more than a certain similarity level alpha;
and 4, step 4: the retrieved original big data analysis model belongs to which original big data analysis model class; if the target raw big data analysis model belongs to a plurality of classes, the class with the minimum average distance is the class to which the target raw big data analysis model belongs;
and 5: recommending the original big data analysis model by using a classical fuzzy average clustering algorithm;
step 6: if matching the similarity value JmixIf the new big data analysis model result is larger than a given threshold and the new big data analysis model result is not affiliated to any cluster subset under the classification threshold, adding the new big data analysis model result and the original big data analysis model into an original big data analysis model base to automatically complete the learning of the original big data analysis model;
and 7: after the user inputs the intermediate storage system, the user identity prompts the user to pass, and after the user inputs the data format, the storage interface is called and a commodity information storage transaction instruction is sent to the storage interface; wherein the data format comprises a general part and a secret part;
and 8: and after receiving the transaction saving instruction, acquiring the public account registered on the commodity recommendation system, logging in the commodity recommendation system, and sending the saving instruction to the commodity recommendation system after passing the verification.
5. The method for commodity retrieval and commodity recommendation based on a decentralized big data retrieval market according to claim 4, wherein the method for establishing the original big data analysis model comprises the following steps:
establishing an original big data analysis model system of a quadruple (U, A, V, F); it is composed ofIn, U ═ U1，u2，...，unThe method is characterized in that the method is a set of original big data analysis model entities, and is called a discourse domain; the method comprises the following steps that an original big data analysis model space A is equal to C and U, C is a condition attribute set, and D is a decision set; is a set of global attribute values, VaIs the value range of attribute a; f is a function of UxA → V. The domain of discourse can be divided according to the condition attributes, and the objects in the domain of discourse are divided into decision classes with different decision attributes according to the difference of the condition attributes.
6. The method for commodity retrieval and commodity recommendation based on decentralized big data retrieval market of claim 4, wherein said method for building original big data analysis model further comprises:
the characteristics and the historical retrieval behaviors of the user are attributed to a condition attribute set, the historical purchase records of the user are attributed to a decision set, and any one of the original mature original big data analysis models is represented by m characteristic indexes, namely C ═ a1，a2，...，am}，D＝{f1(r1，r2，...，rD)，f2(r1，r2，...，rp)，...fq(r1，r2，...，rp) In which aiIs the condition attribute of the historical user characteristic and the retrieval behavior, fi is the historical purchase record, belongs to the decision set or the recommendation set, riAre the relevant object attributes of the purchase record.
7. The commodity retrieval and commodity recommendation method based on the decentralized big data retrieval market of claim 4, wherein in step 8, after the commodity recommendation system receives the save instruction, the trading interface is called, a trading record is created in the commodity recommendation system, and a special trading value is created at the same time; the commodity recommending system starts an internal public account B to automatically create a transaction according to the special transaction value, and the value ensures the successful transaction, so that data is written into the commodity recommending system, and a transaction unique ID is sent to the information storage unit;
the information storage unit stores and backs up the transaction unique ID, corresponds to the ID of the commodity and stores a special transaction value, and then sends the transaction unique ID and the ID of the commodity to the display module;
the display module displays the unique ID of the transaction and the ID of the commodity;
when a user inputs a transaction unique ID or a commodity ID, the information retrieval module obtains a special transaction value in the information storage unit, then a history inquiry interface is called, the special transaction value is sent to a commodity recommendation system, the commodity recommendation system inquires history data, and the commodity recommendation system data is sent to the display unit; the display unit displays the inquired commodity information in the commodity recommendation system to the user.
8. The method for commodity search and commodity recommendation based on decentralized big data search market according to claim 3, wherein after said step three, further performing: and after the user-related behavior data of the uploaded information is stored and used for behavior analysis, the big data retrieval market settles the income of the use of the related data for the user.
9. A computer device, characterized in that the computer device comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to carry out the steps of:
collecting user retrieval behaviors of the retrieval data, and collecting user related behavior data of the uploaded information;
storing and retrieving related data, performing retrieval matching of information based on a big data analysis technology, generating a big data analysis report, and returning the report to a user retrieving data;
based on the big data analysis result, general demand or customized demand related data of the user about the goods or services are provided for the goods service provider, and meanwhile related goods and service information is accurately pushed to the target customer.
10. A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
collecting user retrieval behaviors of the retrieval data, and collecting user related behavior data of the uploaded information;
storing and retrieving related data, performing retrieval matching of information based on a big data analysis technology, generating a big data analysis report, and returning the report to a user retrieving data;
based on the big data analysis result, general demand or customized demand related data of the user about the goods or services are provided for the goods service provider, and meanwhile related goods and service information is accurately pushed to the target customer.
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