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Cognitive Knowledge-aware Social Recommendation via Group-enhanced Ranking Model

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Abstract

Cognitive inspired recommendation systems have attracted increasing attention in recent years, aiming at fitting user ratings on certain items. However, the performance of recommendation approaches has been limited due to the sparsity and ambiguity of cognitive knowledge user-item ratings. Top-k recommendation has therefore been addressed and has become one of the most popular research areas. The goal of top-k recommendation is to capture the relative preferences of users and fit the optimal ranking list of items. Meanwhile, the development of social networks provides a new way to model user preferences to improve the accuracy and interpretation ability of cognition-aware recommendation models. To integrate user social information into top-k recommendation, we propose a group-enhanced ranking method based on matrix factorization. In our method, we first compute trust values between users based on user trust relationships. Then, we incorporate a trust matrix into the loss function with a social-based penalty term that reduces the distances between preference vectors of trusted users. Experimental results on two real datasets from Epinions and BaiduMovies show that the proposed method outperforms several state-of-the-art methods in terms of the normalized discounted cumulative gain (NDCG) value. Our model effectively improves the accuracy of social recommendations. We propose a novel cognitive knowledge-aware group-enhanced social recommendation method for item recommendation. The model modifies the loss function by considering the user trust relationship and group-enhanced ranking and significantly improves the performance of social recommendations.

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References

  1. Ricci F, Rokach L, Shapira B. Introduction to recommender systems handbook. In: recommender systems handbook. Springer; 2011. p. 1–35.

  2. Herlocker J, Konstan JA, Riedl J. An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf Retr. 2002;5(4):287–310.

    Article  Google Scholar 

  3. Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer. 2009;42(8).

  4. Jiang M, Cui P, Liu R, Yang Q, Wang F, Zhu W, et al. Social contextual recommendation. In: Proceedings of the 21st ACM international conference on Information and knowledge management. ACM; 2012. p. 45–54.

  5. Yang C, Zhou Y, Chen L, Zhang X, Chiu DM. Social-group-based ranking algorithms for cold-start video recommendation. Journal of Data Science. 2016;1:165–75.

    Google Scholar 

  6. Liu, X. Towards Context-Aware Social Recommendation via Trust Networks. International Conference on Web Information Systems Engineering; 2013. p. 121–134.

  7. Karatzoglou A, Baltrunas L, Shi Y. Learning to rank for recommender systems. In: Proceedings of the 7th ACM Conference on Recommender Systems. ACM; 2013. p. 493–494.

  8. Mnih A, Salakhutdinov RR. Probabilistic matrix factorization. In: Advances in neural information processing systems. 2008. p. 1257–1264.

  9. Li Y, Lin L, Hsieh C, Huang B. A social investing approach for portfolio recommendation. Inf Manag. 2021;58(8):103536.

  10. Salakhutdinov R, Mnih A. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the 25th International Conference on Machine learning. ACM; 2008. p. 880–887.

  11. Ren X, Song M, Haihong E, Song J. Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation. Neurocomputing. 2017;241:38–55.

    Article  Google Scholar 

  12. Hernando A, Bobadilla J, Ortega F. A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model. Knowl-Based Syst. 2016;97(97):188–202.

    Article  Google Scholar 

  13. Du X, Liu H, Jing L. Additive Co-clustering with social influence for recommendation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems. ACM; 2017. p. 193–200.

  14. Kargar M, Lin Z. A socially motivating and environmentally friendly tour recommendation framework for tourist groups. Expert Syst Appl. 2021;180:115083.

  15. Stoica A, Riederer CJ, Chaintreau A. Algorithmic Glass Ceiling in Social Networks: The effects of social recommendations on network diversity. international world wide web conferences. 2018:923–932.

  16. Liu H, Jing L, Wen J, Xu P, Yu J, Ng MK. Bayesian additive matrix approximation for social recommendation. ACM Trans Knowl Discov Data. 2022;16(1):7:1–7:34.

  17. Wang X, He X, Nie L, Chua T. Item silk road: recommending items from information domains to social users. International ACM SIGIR Conference on Research and Development in Information Retrieval. 2017:185–194.

  18. Liu C, Zhou C, Wu J, Hu Y, Guo L. Social recommendation with an essential preference space. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, February 2–7, 2018. 2018.

  19. Meng X, Wang S, Shu K, Li J, Chen B, Liu H, et al. Personalized privacy-preserving social recommendation. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, February 2-7, 2018. 2018.

  20. Rafailidis D, Kefalas P, Manolopoulos Y. Preference dynamics with multimodal user-item interactions in social media recommendation. Expert Systems with Applications. 2017;74:11–8.

    Article  Google Scholar 

  21. Ke G, Du H, Chen Y. Cross-platform dynamic goods recommendation system based on reinforcement learning and social networks. Appl Soft Comput. 2021;104:107213.

  22. Salamat A, Luo X, Jafari A. HeteroGraphRec: A heterogeneous graph-based neural networks for social recommendations. Knowl Based Syst. 2021;217:106817.

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

  24. 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. ACM; 2010. p. 135–142.

  25. Wu L, Chen E, Liu Q, Xu L, Bao T, Zhang L. Leveraging tagging for neighborhood-aware probabilistic matrix factorization. In: Proceedings of the 21st ACM international conference on Information and knowledge management. ACM; 2012. p. 1854–1858.

  26. Gohari FS, Aliee FS, Haghighi H. A new confidence-based recommendation approach: combining trust and certainty. Inf Sci. 2018;422:21–50.

    Article  Google Scholar 

  27. Jamali M, Ester M. A matrix factorization technique with trust propagation for recommendation in social networks. In: ACM Conference on Recommender Systems. 2010. p. 135–142.

  28. Liu Z, Ma J, Jiang Z, Miao Y. FCT: a fully-distributed context-aware trust model for location based service recommendation. Science in China Series F: Information Sciences. 2017;60(8):082102.

  29. Deng S, Huang L, Xu G, Wu X, Wu Z. On deep learning for trust-aware recommendations in social networks. IEEE Trans Neural Netw. 2017;28(5):1164–77.

    Article  Google Scholar 

  30. Barbu C, Ziegler J. Co-Staying: a social network for increasing the trustworthiness of hotel recommendations. In: ACM Conference on Recommender Systems. 2017. p. 35–39.

  31. Abdelkhalek R. Improving the trustworthiness of recommendations in collaborative filtering under the belief function framework. In: Eleventh ACM Conference on Recommender Systems. 2017. p. 421–425.

  32. Azadjalal MM, Moradi P, Abdollahpouri A, Jalili M. A trust-aware recommendation method based on Pareto dominance and confidence concepts. Knowl-Based Syst. 2017;116:130–43.

    Article  Google Scholar 

  33. Guo G, Zhang J, Zhu F, Wang X. Factored similarity models with social trust for top-N item recommendation. Knowl-Based Syst. 2017;122(122):17–25.

    Article  Google Scholar 

  34. Park C, Kim DH, Oh J, Yu H. Improving top-K recommendation with truster and trustee relationship in user trust network. Inf Sci. 2016;374:100–14.

    Article  Google Scholar 

  35. Liu NN, Yang Q. Eigenrank: a ranking-oriented approach to collaborative filtering. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM; 2008. p. 83–90.

  36. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L. BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. AUAI Press; 2009. p. 452–461.

  37. Burges C, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N, et al. Learning to rank using gradient descent. In: Proceedings of the 22nd international conference on Machine learning. ACM; 2005. p. 89–96.

  38. Liu NN, Cao B, Zhao M, Yang Q. Adapting neighborhood and matrix factorization models for context aware recommendation. In: Proceedings of the Workshop on Context-Aware Movie Recommendation. ACM; 2010. p. 7–13.

  39. Marden JI. Analyzing and modeling rank data. Chapman and Hall/CRC; 2014.

  40. Weimer M, Karatzoglou A, Le QV, Smola AJ. Cofi rank-maximum margin matrix factorization for collaborative ranking. In: Advances in neural information processing systems. 2008. p. 1593–1600.

  41. Järvelin K, Kekäläinen J. IR evaluation methods for retrieving highly relevant documents. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM; 2000. p. 41–48.

  42. Järvelin K, Kekäläinen J. Cumulated gain-based evaluation of IR techniques. ACM Trans Inf Syst (TOIS). 2002;20(4):422–46.

    Article  Google Scholar 

  43. Shi Y, Larson M, Hanjalic A. List-wise learning to rank with matrix factorization for collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems. ACM; 2010. p. 269–272.

  44. Cao Z, Qin T, Liu TY, Tsai MF, Li H. Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the 24th International Conference on Machine Learning. ACM; 2007. p. 129–136.

  45. Li Y, Yang L, Xu B, Wang J, Lin H. Improving user attribute classification with text and social network attention. Cogn Comput. 2019.

  46. Zhou D, Hofmann T, Schölkopf B. Semi-supervised learning on directed graphs. In: Advances in neural information processing systems; 2005. p. 1633–1640.

  47. Massa P, Avesani P. Trust-aware recommender systems. In: Proceedings of the 2007 ACM Conference on Recommender Systems. ACM; 2007. p. 17–24.

  48. Lin Y, Lin H, Ye Z, Jin S, Sun X. Learning to rank with groups. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management. ACM; 2010. p. 1589–1592.

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Acknowledgements

This work was partially supported by a grant from the Natural Science Foundation of China (No. 62006034), the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China

    Bo Xu, Hongfei Lin, Liang Yang & Kan Xu

  2. WISE Lab, School of Public Administration and Law, Dalian University of Technology, Dalian, China

    Yuan Lin

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  1. Bo Xu
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  2. Hongfei Lin
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  3. Liang Yang
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  4. Yuan Lin
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  5. Kan Xu
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Correspondence to Bo Xu.

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Xu, B., Lin, H., Yang, L. et al. Cognitive Knowledge-aware Social Recommendation via Group-enhanced Ranking Model. Cogn Comput 14, 1055–1067 (2022). https://doi.org/10.1007/s12559-022-10001-x

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