Recommendation system in social networks with topical attention and probabilistic matrix factorization

Autoři: Weiwei Zhang aff001;  Fangai Liu aff001;  Daomeng Xu aff001;  Lu Jiang aff001
Působiště autorů: School of Information Science and Engineering, Shandong Normal University, Jinan, China aff001
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223967


Collaborative filtering (CF) is a common recommendation mechanism that relies on user-item ratings. However, the intrinsic sparsity of user-item rating data can be problematic in many domains and settings, limiting the ability to generate accurate predictions and effective recommendations. At present, most algorithms use two-valued trust relationship of social network to improve recommendation quality but fail to take into account the difference of trust intensity of each friend and user’s comment information. To this end, the recommendation system within a social network adopts topical attention and probabilistic matrix factorization (STAPMF) is proposed. We combine the trust information in social networks and the topical information from review documents by proposing a novel algorithm combining probabilistic matrix factorization and attention-based recurrent neural networks to extract item underlying feature vectors, user’s personal potential feature vectors, and user’s social hidden feature vectors, which represent the features extracted from the user’s trusted network. Using real-world datasets, we show a significant improvement in recommendation performance comparing with the prevailing state-of-the-art algorithms for social network-based recommendation.

Klíčová slova:

Algorithms – Attention – Eigenvectors – Interpersonal relationships – Neural networks – Recurrent neural networks – Social networks – Data mining


1. Peng M, Zeng G, Sun Z, Huang J, Wang H, & Tian G. Personalized app recommendation based on app permissions. World Wide Web.2018;21(1):89–104.

2. Sarwar B., Karypis G., Konstan J., & Riedl J. Item-based collaborative filtering recommendation algorithms. Www. 2001;1: 285–295.

3. Linden G, Smith B, York J. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing. 2003;(1): 76–80.

4. Elmongui, H. G., Mansour, R., Morsy, H., Khater, S., El-Sharkasy, A., & Ibrahim, R. TRUPI: Twitter recommendation based on users’ personal interests, International Conference on Intelligent Text Processing and Computational Linguistics. Springer, Cham.2015: 272–284.

5. Davidson, J., Livingston, B., Sampath, D., Liebald, B., Liu, J., & Nandy, P., et al. The YouTube video recommendation system, Proceedings of the fourth ACM conference on Recommender systems. ACM.2010: 293–296.

6. Xing S., Liu F., Zhao X., & Li T. Points-of-interest recommendation based on convolution matrix factorization. Applied intelligence. 2018; 48(8):2458–2469.

7. Zhang, J., Lin, Z., Xiao, B., & Zhang, C. An optimized item-based collaborative filtering recommendation algorithm. In Network Infrastructure and Digital Content, 2009. IC-NIDC 2009. IEEE International Conference.2009: 414–418.

8. Sa L. Collaborative filtering recommendation algorithm based on cloud model clustering of multi-indicators item evaluation, 2011 International Conference on Business Computing and Global Informatization. IEEE. 2011: 645–648.

9. Min P., Qianqian X., Hua W., Yanchun Z., & Gang T. Bayesian sparse topical coding, IEEE Transactions on Knowledge and Data Engineering. 2018; 31(6): 1080–1093.

10. Tang, J., Gao, H., Hu, X., & Liu, H. Exploiting homophily effect for trust prediction. In Proceedings of the sixth ACM international conference on Web search and data mining. 2013: 53–62.

11. Zhang J., & Curley S. P. Exploring explanation effects on consumers’ trust in online recommender agents. International Journal of Human–Computer Interaction.2018;34(5): 421–432.

12. Choudhary N, Bharadwaj K K. Leveraging Trust Behaviour of Users for Group Recommender Systems in Social Networks, Integrated Intelligent Computing, Communication and Security. Springer, Singapore. 2019: 41–47.

13. Almahairi, A., Kastner, K., Cho, K., & Courville, A. Learning distributed representations from reviews for collaborative filtering. In Proceedings of the 9th ACM Conference on Recommender Systems.2015:147–154.

14. Ling, G., Lyu, M. R., & King, I. Ratings meet reviews, a combined approach to recommend. In Proceedings of the 8th ACM Conference on Recommender systems.2014: 105–112.

15. Peng M., Zhu J., Wang H., Li X., Zhang Y., Zhang X., & Tian G. Mining event-oriented topics in microblog stream with unsupervised multi-view hierarchical embedding. ACM Transactions on Knowledge Discovery from Data (TKDD). 2018;12(3): 38.

16. Ren, Z., Liang, S., Li, P., Wang, S., & de Rijke, M. Social collaborative viewpoint regression with explainable recommendations. In Proceedings of the tenth ACM international conference on web search and data mining.2017: 485–494.

17. Lu, Y., Dong, R., & Smyth, B. Coevolutionary Recommendation Model: Mutual Learning between Ratings and Reviews. In Proceedings of the 2018 World Wide Web Conference on World Wide Web.2018:773–782.

18. Zheng, L., Noroozi, V., & Yu, P. S. Joint deep modeling of users and items using reviews for recommendation. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining.2017: 425–434.

19. Peng, M., Xie, Q., Zhang, Y., Wang, H., Zhang, X., Huang, J., & Tian, G. Neural sparse topical coding, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018: 2332–2340.

20. Peng, M., Chen, D., Xie, Q., Zhang, Y., Wang, H., Hu, G., … & Zhang, Y. Topic-net conversation model, International Conference on Web Information Systems Engineering. Springer, Cham.2018: 483–496.

21. Wang, H., Wang, N., & Yeung, D. Y. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2015: 1235–1244.

22. Wang, H., Xingjian, S. H. I., & Yeung, D. Y. Collaborative recurrent autoencoder: Recommend while learning to fill in the blanks. In Advances in Neural Information Processing Systems.2016: 415–423

23. Xu, Y., Lam, W., & Lin, T. Collaborative filtering incorporating review text and co-clusters of hidden user communities and item groups. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. 2014: 251–260.

24. Xu, Y., Shi, B., Tian, W., & Lam, W. A unified model for unsupervised opinion spamming detection incorporating text generality. In Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015:725–731.

25. Mnih A, Salakhutdinov R R. Probabilistic matrix factorization, Advances in neural information processing systems. 2008: 1257–1264.

26. Mikolov, T., Chen, K., Corrado, G., & Dean, J. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.

27. Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y. N. Convolutional sequence to sequence learning, Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. Org.2017: 1243–1252.

28. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N. et al. Attention is all you need, Advances in neural information processing systems. 2017: 5998–6008.

29. Xing S., Wang Q., Zhao X., & Li T. A hierarchical attention model for rating prediction by leveraging user and product reviews. Neurocomputing. 2019; 332: 417–427.

30. Sainath T N, Kingsbury B, Sindhwani V, et al. Low-rank matrix factorization for deep neural network training with high-dimensional output targets, 2013 IEEE international conference on acoustics, speech and signal processing. IEEE.2013: 6655–6659.

31. Salakhutdinov R, Mnih A, Hinton G. Restricted Boltzmann machines for collaborative filtering, Proceedings of the 24th international conference on Machine learning. ACM. 2007: 791–798.

32. D. Bahdanau, K. Cho, Y. Bengio, Neural Machine Translation by Jointly Learning to Align and Translate, 2014, ArXiv:1409.0473.

33. Kim, D., Park, C., Oh, J., Lee, S., & Yu, H. Convolutional matrix factorization for document context-aware recommendation, Proceedings of the 10th ACM Conference on Recommender Systems. ACM. 2016: 233–240.

34. X. Wang, L. Yu, K. Ren, G. Tao, W. Zhang, Dynamic attention deep model for article recommendation by learning human editors demonstration, in: Proceedings of the 23th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017: 2051–2059.

35. Seo, S., Huang, J., Yang, H., & Liu, Y. Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In Proceedings of the Eleventh ACM Conference on Recommender Systems. 2017: 297–305.

36. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 2013: 3111–3119.

37. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[J]. arXiv preprint arXiv:1406.1078, 2014.

38. Tang, J., Gao, H., & Liu, H. mTrust: discerning multi-faceted trust in a connected world. In Proceedings of the fifth ACM international conference on Web search and data mining.2012:93–102.

39. Ma, H., Yang, H., Lyu, M. R., & King, I. Sorec: social recommendation using probabilistic matrix factorization. In Proceedings of the 17th ACM conference on Information and knowledge management. 2008:931–940.

40. Ma, H., Zhou, D., Liu, C., Lyu, M. R., & King, I. Recommender systems with social regularization. In Proceedings of the fourth ACM international conference on Web search and data mining.2011:287–296.

41. Ma, H., King, I., & Lyu, M. R. Learning to recommend with social trust ensemble. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval.2009:203–210.

42. Jamali, M., & Ester, M. A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the fourth ACM conference on Recommender systems.2010:135–142.

43. Yao, W., He, J., Huang, G., & Zhang, Y. Modeling dual role preferences for trust-aware recommendation. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval.2014:975–978.

44. Wang, C., & Blei, D. M. Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining.2011:448–456.

45. Rafailidis, D., & Crestani, F. Recommendation with Social Relationships via Deep Learning. In Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval.2017:151–158.

Článek vyšel v časopise


2019 Číslo 10