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An initialization method for the latent vectors in probabilistic matrix factorization for sparse datasets

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Abstract

Recommendation-based e-commerce applications have been utilized by many companies to increase their sales performance. Probabilistic matrix factorization (PMF) is a widely-used method for collaborative filtering in recommendation systems. Although the method’s performance has been demonstrated successfully in many challenging datasets including Netflix, they are not able to perform well in large sparse datasets where there is considerably low number of rating information. As a remedy, numerous advancements of PMF were proposed which incorporated side information into latent vectors as priors in order to ensure richer prior information in them. However, in cases where such side information is inaccessible, PMF-based algorithms do not perform well. In this study, we propose two new initialization methods for PMF which take into consideration the distribution statistics of user product ratings to enrich latent vectors. The experiments show that the proposed solutions give better results to those in the literature in very sparse datasets.

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References

  1. Adams R, Dahl G, Murray I (2010) Incorporating side information in probabilistic matrix factorization with Gaussian processes. In: Proceedings of the twenty-sixth annual conference on uncertainty in artificial intelligence (UAI-10). AUAI Press, Corvallis, pp 1–9

  2. Balakrishnan S, Chopra S (2012) Two of a kind or the ratings game? Adaptive pairwise preferences and latent factor models. Front Comput Sci 6(2):197–208

    Article  MathSciNet  Google Scholar 

  3. Chen M, Singh JP (2001) Computing and using reputations for internet ratings. In: Proceedings of the 3rd ACM conference on electronic commerce. ACM, Tampa, pp 154–162

  4. Douban Dataset @ Web Intelligence and Social Computing Lab (2013) https://www.cse.cuhk.edu.hk/irwin.king/pub/data/douban. Accessed 25 Sept 2013

  5. Epinions Dataset (2016) http://www.trustlet.org/downloaded_epinions.html. Accessed 20 Mar 2016

  6. Gaeta M, Orciuoli F, Rarità L, Tomasiello S (2017) Fitted Q-iteration and functional networks for ubiquitous recommender systems. Soft Comput 21(23):7067–7075

    Article  Google Scholar 

  7. Guo Q, Liu J, Wang B (2009) Improved collaborative filtering algorithm based on heat conduction. Front Comput Sci China 3(3):417–420

    Article  Google Scholar 

  8. Kaczmarek P, Słysz A (2018) Applying matrix factorization techniques to compare experts’ categorization process during case formulation task performed by concept maps. Cogn Syst Res 47:173–185

    Article  Google Scholar 

  9. Katarya R, Verma OP (2016) Recommender system with grey wolf optimizer and FCM. Neural Comput Appl 30:1679–1687

    Article  Google Scholar 

  10. Koren Y, Bell R (2011) Advances in collaborative filtering. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender systems handbook. Springer, New York, pp 145–186

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  12. Langseth H, Nielsen TD (2015) Scalable learning of probabilistic latent models for collaborative filtering. Decis Support Syst 74:1–11

    Article  Google Scholar 

  13. Li F, Guandong X, Cao L (2016) Two-level matrix factorization for recommender systems. Neural Comput Appl 27(8):2267–2278

    Article  Google Scholar 

  14. Liu J, Caihua W, Liu W (2013) Bayesian probabilistic matrix factorization with social relations and item contents for recommendation. Decis Support Syst 55(3):838–850

    Article  Google Scholar 

  15. Luo X, Liu H, Gou G, Xia Y, Zhu Q (2012) A parallel matrix factorization based recommender by alternating stochastic gradient decent. Eng Appl Artif Intell 25(7):1403–1412

    Article  Google Scholar 

  16. Ma H, King I, Lyu MR (2011) Learning to recommend with explicit and implicit social relations. ACM Trans Intell Syst Technol (TIST) 2(3):29

    Google Scholar 

  17. Ma H, Zhou TC, Lyu MR, King I (2011) Improving recommender systems by incorporating social contextual information. ACM Trans Inf Syst (TOIS) 29(2):9

    Article  Google Scholar 

  18. Ma H, Yang H, Lyu MR, King I (2008) SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM conference on Information and knowledge management. ACM, Napa Valley, pp 931–940

  19. Ma H, Zhou D, Liu C, Lyu MR, King I (2011) Recommender systems with social regularization. In: Proceedings of the fourth ACM international conference on Web search and data mining. ACM, New York, pp 287–296

  20. Mnih A, Salakhutdinov R (2007) Probabilistic matrix factorization. In: Platt JC, Koller D, Singer Y, Roweis ST (eds) Advances in neural information processing systems. Neural Information Processing Systems Foundation, Inc., pp 1257–1264

  21. Movielens Dataset (2013) http://grouplens.org/datasets/movielens/. Accessed 15 Dec 2013

  22. Murat YA, Tevfik A, Gurgen Fikret S (2017) Scalable and adaptive collaborative filtering by mining frequent item co-occurrences in a user feedback stream. Eng Appl Artif Intell 58:171–184

    Article  Google Scholar 

  23. Najafabadi MK, Mohamed AH, Mahrin MN (2019) A survey on data mining techniques in recommender systems. Soft Comput 23(2):627–654

    Article  Google Scholar 

  24. O’Donovan J, Smyth B (2005) Trust in recommender systems. In: Proceedings of the 10th international conference on intelligent user interfaces. ACM, San Diego, pp 167–174

  25. Porteous I, Asuncion AU, Welling M (2010) Bayesian matrix factorization with side information and Dirichlet process mixtures. In: AAAI

  26. Ranjbar M, Moradi P, Azami M, Jalili M (2015) An imputation-based matrix factorization method for improving accuracy of collaborative filtering systems. Eng Appl Artif Intell 46:58–66

    Article  Google Scholar 

  27. Salakhutdinov R, Mnih A (2008) Probabilistic matrix factorization. Adv Neural Inf Process Syst 20:1257–1264

    Google Scholar 

  28. Salakhutdinov R, Mnih A (2008) Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the 25th international conference on machine learning. ACM, Helsinki, pp 880–887

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

  30. Shi Y, Larson M, Hanjalic A (2014) Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges. ACM Comput Surv CSUR 47(1):3

    Google Scholar 

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

  32. Xue L, Zhang P, Zeng A (2019) Enhancing the long-term performance of recommender system. Phys A Stat Mech Appl 531:121731

    Article  Google Scholar 

  33. Yueshen X, Yin J (2015) Collaborative recommendation with user generated content. Eng Appl Artif Intell 45:281–294

    Article  Google Scholar 

  34. Yu K, Zhu S, Lafferty J, Gong Y (2009) Fast nonparametric matrix factorization for large-scale collaborative filtering. In: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 211–218

  35. Zhao Z-L, Wang C-D, Wan Y-Y, Lai J-H (2017) Recommendation in feature space sphere. Electron Commer Res Appl 26:109–118

    Article  Google Scholar 

  36. Zhou T, Shan H, Banerjee A, Sapiro G (2012) Kernelized probabilistic matrix factorization: exploiting graphs and side information. In: SIAM international conference on data mining (SDM), vol 12, pp 403–414

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Acknowledgements

We thank Tugba T. Temizel for the comments that greatly improved the manuscript. She was also the supervisor of the Ph.D thesis that is the base of this paper.

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  1. Computer Engineering Department, Ankara University, Ankara, Turkey

    Yilmaz Ar

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  1. Yilmaz Ar
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Appendix

Appendix

See Table 7.

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Ar, Y. An initialization method for the latent vectors in probabilistic matrix factorization for sparse datasets. Evol. Intel. 13, 269–281 (2020). https://doi.org/10.1007/s12065-019-00299-2

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