Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

DFIAM: deep factorization integrated attention mechanism for smart TV recommendation

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Users are frequently overwhelmed by their uninterested programs due to the development of smart TV and the excessive number of programs. For addressing this issue, various recommendation methods have been introduced to TV fields. In TV content recommendation, auxiliary information, such as users’ personality traits and program features, greatly influences their program preferences. However, existing methods always fail to take auxiliary information into account. In this paper, aiming at personality program recommendation on smart TV platforms, we propose a novel Deep Factorization Integrated Attention Mechanism (DFIAM) model, which fully takes advantage of users’ personality traits, program and interaction features to construct users’ preference representations. DFIAM consists of two components, FNN component and DMF component. By suitably exploiting auxiliary information, FNN component devises a feature-interaction layer to capture the low- and higher-order feature interactions, while DMF component has a field-interaction layer to acquire higher-order field interactions. The embedding layer is divided into two layers , including feature embedding layer and field embedding layer. The two components share the feature embedding layer to profile latent representations of user and program features to reduce learning parameters and computational complexity. And the field embedding layer calculated by feature embedding layer is the input of DMF component. Besides, hierarchical attention networks are applied to self-adapt the influence of each feature and effectively capture more important feature interactions. To evaluate the performance of the DFIAM model, extensive experiments are conducted on two real-world datasets from different scenarios. The results of our proposed model have outperformed the mainstream neural network-based recommendation models in terms of RMSE, MAE and R-square.

This is a preview of subscription content,log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Asabere, N. Y., Acakpovi, A.: Roppsa: Tv program recommendation based on personality and social awareness. Math. Probl. Eng. (2020)

  2. Cheng, H. T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., Ispir, M., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st workshop on deep learning for recommender systems, pp. 7–10 (2016)

  3. Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: Deepfm: a factorization-machine based neural network for ctr prediction. arXiv:1703.04247 (2017)

  4. Harper, F.M., Konstan, J.A.: The movielens datasets: History and context. Acm Trans Interact Intell Syst5(4), 1–19 (2015)

    Article  Google Scholar 

  5. He, X., Chua, T. S.: Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 355–364 (2017)

  6. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T. S.: Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pp. 173–182 (2017)

  7. He, X., Zhang, H., Kan, M. Y., Chua, T. S.: Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 549–558 (2016)

  8. Hong, F., Huang, D., Chen, G.: Interaction-aware factorization machines for recommender systems. Proc. AAAI Conf. Artif. Intell.33, 3804–3811 (2019)

    Google Scholar 

  9. Juan, Y., Zhuang, Y., Chin, W. S., Lin, C. J.: Field-aware factorization machines for ctr prediction. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 43–50 (2016)

  10. Kipf, T. N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016)

  11. Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434, Las Vegas (2008)

  12. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer42(8), 30–37 (2009)

    Article  Google Scholar 

  13. Li, W., Xu, B.: Aspect-based fashion recommendation with attention mechanism. IEEE Access8(8), 141814–141823 (2020)

  14. Li, Y., Liu, M., Yin, J., Cui, C., Nie, L.: Routing micro-videos via a temporal graph-guided recommendation system. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 1464–1472 (2019)

  15. Lian, J., Zhou, X., Zhang, F., Chen, Z., Xie, X., Sun, G.: xdeepfm: Combining explicit and implicit feature interactions for recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1754–1763 (2018)

  16. Ni, Y., Ou, D., Liu, S., Li, X., Ou, W., Zeng, A., Si, L.: Perceive your users in depth: Learning universal user representations from multiple e-commerce tasks. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 596–605 (2018)

  17. Phelan, O., Mccarthy, K., Smyth, B.: Using twitter to recommend real-time topical news. In: Proceedings of the third ACM conference on Recommender systems, pp. 385–388. RecSys 2009, New York

  18. Pyo, S., Kim, E., Kim, M.: Automatic and personalized recommendation of tv program contents using sequential pattern mining for smart tv user interaction. Multimed. Syst.19(6), 527–542 (2013)

    Article  Google Scholar 

  19. Rendle, S.: Factorization Machines. In: 2010 IEEE International Conference on Data Mining, pp. 995–1000. IEEE (2010)

  20. Rendle, S., Schmidt-Thieme, L.: Pairwise interaction tensor factorization for personalized tag recommendation. In: Proceedings of the third ACM international conference on Web search and data mining, pp. 81–90 (2010)

  21. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp. 285–295 (2001)

  22. Song, W., Shi, C., Xiao, Z., Duan, Z., Xu, Y., Zhang, M., Tang, J.: Autoint: Automatic feature interaction learning via self-attentive neural networks. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1161–1170 (2019)

  23. Véras, D., Prota, T., Bispo, A., Prudêncio, R., Ferraz, C.: A literature review of recommender systems in the television domain. Expert Syst. Appl.42(22), 9046–9076 (2015)

    Article  Google Scholar 

  24. Wang, D., Zhang, X., Yu, D., Xu, G., Deng, S.: Came: Content-and context-aware music embedding for recommendation. IEEE Transactions on Neural Networks and Learning Systems (2020)

  25. Wen, H., Zhang, J., Wang, Y., Lv, F., Bao, W., Lin, Q., Yang, K.: Entire space multi-task modeling via post-click behavior decomposition for conversion rate prediction. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2377–2386 (2020)

  26. Xiao, J., Ye, H., He, X., Zhang, H., Wu, F., Chua, T. S.: Attentional factorization machines: Learning the weight of feature interactions via attention networks. arXiv:1708.04617 (2017)

  27. Xue, H. J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep Matrix Factorization Models for Recommender Systems. In: Twenty-Sixth International Joint Conference on Artificial Intelligence, vol. 17, pp. 3203–3209, Melbourne (2017)

  28. Zhang, H., Shen, F., Liu, W., He, X., Luan, H., Chua, T. S.: Discrete collaborative filtering. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 325–334 (2016)

  29. Zhang, W., Du, T., Wang, J.: Deep Learning over Multi-Field Categorical Data. In: European Conference on Information Retrieval. Springer, pp. 45–57 (2016)

  30. Zhao, Z., Hong, L., Wei, L., Chen, J., Nath, A., Andrews, S., Kumthekar, A., Sathiamoorthy, M., Yi, X., Chi, E.: Recommending what video to watch next: a multitask ranking system. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 43–51 (2019)

  31. Zhou, X., Yue, X., Li, Y., Josang, A., Cox, C.: The state-of-the-art in personalized recommender systems for social networking. Artif. Intell. Rev.37(2), 119–132 (2012)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Key Research and Development Program of Zhejiang Province, China(Grant No.2019C03138).

Author information

Authors and Affiliations

  1. Institute of Intelligent Media Technology, Communication University of Zhejiang, Hangzhou, China

    Yijie Zhou & Dingguo Yu

  2. Key lab of Film and TV Media Technology of Zhejiang Province, Hangzhou, China

    Yijie Zhou & Dingguo Yu

  3. College of Media Engineering, Communication University of Zhejiang, Hangzhou, China

    Xuewen Shen, Suiyu Zhang & Dingguo Yu

  4. School of Computer Science, University of Technology Sydney, Sydney, Australia

    Guandong Xu

Authors
  1. Yijie Zhou
  2. Xuewen Shen
  3. Suiyu Zhang
  4. Dingguo Yu
  5. Guandong Xu

Corresponding author

Correspondence toDingguo Yu.

Ethics declarations

Competing interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Y., Shen, X., Zhang, S.et al. DFIAM: deep factorization integrated attention mechanism for smart TV recommendation.World Wide Web24, 1465–1481 (2021). https://doi.org/10.1007/s11280-021-00924-0

Download citation

Keywords

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Advertisement

[8]ページ先頭
©2009-2025 Movatter.jp