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A social recommender system using item asymmetric correlation

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Abstract

Recommender systems have been one of the most prominent information filtering techniques during the past decade. However, they suffer from two major problems, which degrade the accuracy of suggestions: data sparsity and cold start. The popularity of social networks shed light on a new generation of such systems, which is called social recommender system. These systems act promisingly in solving data sparsity and cold start issues. Given that social relationships are not available to every system, the implicit relationship between the items can be an adequate option to replace the constraints. In this paper, we explored the effect of combining the implicit relationships of the items and user-item matrix on the accuracy of recommendations. The new Item Asymmetric Correlation (IAC) method detects the implicit relationship between each pair of items by considering an asymmetric correlation among them. Two dataset types, the output of IAC and user-item matrix, are fused into a collaborative filtering recommender via Matrix Factorization (MF) technique. We apply the two mostly used mapping models in MF, Stochastic Gradient Descent and Alternating Least Square, to investigate their performances in the presence of sparse data. The experimental results of real datasets at four levels of sparsity demonstrate the better performance of our method comparing to the other commonly used approaches, especially in handling the sparse data.

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References

  1. Guo L, Ma J, Chen Z, Zhong H (2015) Learning to recommend with social contextual information from implicit feedback. Soft Comput 19(5):1351–1362

    Article  Google Scholar 

  2. Tang J, Hu X, Liu H (2013) Social recommendation: a review. Soc Netw Anal Min 3(4):1113–1133

    Article  Google Scholar 

  3. Sivapalan S, Sadeghian A, Rahnama H, Madni AM (2014) Recommender systems in e-commerce. In: 2014 World automation congress (WAC). IEEE, p 2014

  4. Qian X, He F, Zhao G, Mei T (2014) Personalized recommendation combining user interest and social circle. IEEE Trans Knowl Data Eng 26(7):1763–1777

    Article  Google Scholar 

  5. Guy I (2015) Social recommender systems. In: Recommender systems handbook. Springer, pp 511–543

  6. Yang X, Guo Y, Liu Y, Steck H (2014) A survey of collaborative filtering based social recommender systems. Comput Commun 41:1–10

    Article  Google Scholar 

  7. Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109–132

    Article  Google Scholar 

  8. Yuan Q, Li C, Zhao S (2011) Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation. In: Proceedings of the 5th ACM conference on recommender systems. ACM, pp 245–252

  9. Ma H, Zhou D, Liu C, Lyu MR, King I (2011) Recommender systems with social regularization. In: Proceedings of the 4th ACM international conference on Web search and data mining. ACM, pp 287–296

  10. Forsati R, Mahdavi M, Shamsfard M, Sarwat M (2014) Matrix factorization with explicit trust and distrust side information for improved social recommendation. ACM Trans Inf Syst (TOIS) 32(4):17

    Article  Google Scholar 

  11. Koren Y, Bell R, Volinsky C et al (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37

    Article  Google Scholar 

  12. Yu Y, Mo L (2015) Investigating correlation between strength of social relationship and interest similarity. In: International conference on computational social networks. Springer, pp 172–181

  13. Ma H (2014) On measuring social friend interest similarities in recommender systems. In: Proceedings of the 37th international ACM SIGIR conference on research & development in information retrieval. ACM, pp 465–474

  14. Wang D, Ma J, Lian T, Guo L (2014) Recommendation based on weighted social trusts and item relationships. In: Proceedings of the 29th annual ACM symposium on applied computing. ACM, pp 254–259

  15. Sun Z, Guo G, Zhang J (2015) Exploiting implicit item relationships for recommender systems. In: International conference on user modeling, adaptation, and personalization. Springer, pp 252–264

  16. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 426–434

  17. Koren Y, Bell R, Volinsky C et al (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37

    Article  Google Scholar 

  18. Ekstrand MD, Riedl JT, Konstan JA (2011) Collaborative filtering recommender systems. Found Trends Hum Comput Interact 4(2):81–173

    Article  Google Scholar 

  19. Jeong B, Lee J, Cho H (2010) Improving memory-based collaborative filtering via similarity updating and prediction modulation. Inf Sci 180(5):602–612

    Article  Google Scholar 

  20. Bellogin A, Parapar J (2012) Using graph partitioning techniques for neighbour selection in user-based collaborative filtering. In: Proceedings of the 6th ACM conference on recommender systems. ACM, pp 213–216

  21. Krzywicki A, Wobcke W, Kim YS, Cai X, Bain M, Mahidadia A, Compton P (2015) Collaborative filtering for people-to-people recommendation in online dating data analysis and user trial. Int J Hum Comput Stud 76:50–66

    Article  Google Scholar 

  22. Shi Y, Larson M, Hanjalic A (2010) List-wise learning to rank with matrix factorization for collaborative filtering. In: Proceedings of the 4th ACM conference on recommender systems. ACM, pp 269–272

  23. Xiong L, Xi C, Huang T-K, Schneider J, Carbonell JG (2010) Temporal collaborative filtering with bayesian probabilistic tensor factorization. In: Proceedings of the SIAM international conference on data mining. SIAM, p 2010

  24. 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

    Article  Google Scholar 

  25. Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. In: The adaptive Web. Springer, pp 291–324

  26. Wu J, Chen L, Feng Y, Zheng Z, Zhou MC, Wu Z (2013) Predicting quality of service for selection by neighborhood-based collaborative filtering. IEEE Trans Syst Man Cybern Syst 43(2):428–439

    Article  Google Scholar 

  27. Zhao Z-D, Shang M-S (2010) User-based collaborative-filtering recommendation algorithms on hadoop. In: 3rd International conference on knowledge discovery and data mining WKDD’10. IEEE, p 2010

  28. Liu H, Hu Z, Mian A, Tian H, Zhu X (2014) A new user similarity model to improve the accuracy of collaborative filtering. Knowl-Based Syst 56:156–166

    Article  Google Scholar 

  29. Barragáns-martínez AB, Costa-Montenegro E, Burguillo JC, Rey-López M, Mikic-Fonte FA, Peleteiro A (2010) A hybrid content-based and item-based collaborative filtering approach to recommend tv programs enhanced with singular value decomposition. Inf Sci 180(22):4290–4311

    Article  Google Scholar 

  30. Gao M, Wu Z, Jiang F (2011) Userrank for item-based collaborative filtering recommendation. Inf Process Lett 111(9):440–446

    Article  MathSciNet  MATH  Google Scholar 

  31. Wei S, Ye N, Zhang S, Huang X, Zhu J (2012) Collaborative filtering recommendation algorithm based on item clustering and global similarity. In: 5th International conference on business intelligence and financial engineering (BIFE). IEEE , p 2012

  32. Victor P, De Cock M, Cornelis C (2011) Trust and recommendations. In: Recommender systems handbook. Springer, pp 645–675

  33. Golbeck J (2006) Generating predictive movie recommendations from trust in social networks. In: International conference on trust management. Springer, pp 93–104

  34. Nazemian A, Gholami H, Taghiyareh F (2012) An improved model of trust-aware recommender systems using distrust metric. In: Proceedings of the 2012 international conference on advances in social networks analysis and mining (ASONAM 2012). IEEE Computer Society, pp 1079–1084

  35. Luo X, Zhou M, Xia Y, Zhu Q (2014) An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Trans Ind Inf 10(2):1273–1284

    Article  Google Scholar 

  36. Liu J, Wu C, Yi X, Liu W (2014) List-wise probabilistic matrix factorization for recommendation. Inf Sci 278:434–447

    Article  Google Scholar 

  37. Yang S-H, Bo L, Smola A, Sadagopan N, Zheng Z, Zha H (2011) Like like alike: joint friendship and interest propagation in social networks. In: Proceedings of the 20th international conference on World Wide Web. ACM, pp 537–546

  38. Shi Y, Larson M, Hanjalic A (2011) Tags as bridges between domains: improving recommendation with tag-induced cross-domain collaborative filtering. In: International conference on user modeling, adaptation, and personalization. Springer, pp 305–316

  39. Kim J, Park H (2008) Sparse nonnegative matrix factorization for clustering

  40. Webb B (2006) Netflix update: try this at home. Blog post sifter. org/simon/journal/20061211.html

  41. Yu H-F, Hsieh C-J, Si S, Dhillon IS (2014) Parallel matrix factorization for recommender systems. Knowl Inf Syst 41(3):793–819

    Article  Google Scholar 

  42. Niu J, Wang L, Liu X, Yu S (2016) Fuir: fusing user and item information to deal with data sparsity by using side information in recommendation systems. J Netw Comput Appl 70:41–50

    Article  Google Scholar 

  43. Lee K, Lee K (2015) Escaping your comfort zone: a graph-based recommender system for finding novel recommendations among relevant items. Expert Syst Appl 42(10):4851–4858

    Article  Google Scholar 

  44. Zhou X, He J, Huang G, Zhang Y (2015) Svd-based incremental approaches for recommender systems. J Comput Syst Sci 81(4):717–733

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Shahid Beheshti University, G.C, Tehran, Iran

    Arghavan Moradi Dakhel & Hadi Tabatabaee Malazi

  2. School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia

    Mehregan Mahdavi

Authors
  1. Arghavan Moradi Dakhel
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  2. Hadi Tabatabaee Malazi
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  3. Mehregan Mahdavi
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Correspondence to Hadi Tabatabaee Malazi.

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Dakhel, A.M., Malazi, H.T. & Mahdavi, M. A social recommender system using item asymmetric correlation. Appl Intell 48, 527–540 (2018). https://doi.org/10.1007/s10489-017-0973-5

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  • DOI: https://doi.org/10.1007/s10489-017-0973-5

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