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When Factorization Meets Heterogeneous Latent Topics: An Interpretable Cross-Site Recommendation Framework

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Abstract

Data sparsity is a well-known challenge in recommender systems. Previous studies alleviate this problem by incorporating the information within the corresponding social media site. In this paper, we solve this challenge by exploring cross-site information. Specifically, we examine: 1) how to effectively and efficiently utilize cross-site ratings and content features to improve recommendation performance and 2) how to make the recommendation interpretable by utilizing content features. We propose a joint model of matrix factorization and latent topic analysis. Heterogeneous content features are modeled by multiple kinds of latent topics. In addition, the combination of matrix factorization and latent topics makes the recommendation result interpretable. Therefore, the above two issues are simultaneously solved. Through a real-world dataset, where user behaviors in three social media sites are collected, we demonstrate that the proposed model is effective in improving recommendation performance and interpreting the rationale of ratings.

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References

  1. Chen T, Tang L, Liu Q et al. Combining factorization model and additive forest for collaborative followee recommendation. In Proc. 2012 KDD Cup Workshop, Aug. 2012.

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

    Article  Google Scholar 

  3. Salakhutdinov R, Mnih A. Probabilistic matrix factorization. In Proc. the 21st Annual Conference on Neural Information Processing Systems, Dec. 2007, pp.1257–1264.

  4. Töscher A, Jahrer M, Bell R M. The BigChaos solution to the Netflix grand prize. 2009. http://www.netflixprize.com/assets/GrandPrize2009_BPC_BigChaos.pdf, April 2015.

  5. Hu L, Cao J, Xu G, Cao L, Gu Z, Zhu C. Personalized recommendation via cross-domain triadic factorization. In Proc. the 22nd International World Wide Web Conference, May 2013, pp.595–606.

  6. Agarwal D, Chen B C. Regression-based latent factor models. In Proc. the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, June 28–July 1, 2009, pp.19–28.

  7. Xu Z, Tresp V, Rettinger A, Kersting K. Social network mining with nonparametric relational models. In Proc. the 2nd Int. Conf. Advances in Social Network Mining and Analysis, Aug. 2008, pp.77–96.

  8. Sharma A, Cosley D. Do social explanations work? Studying and modeling the effects of social explanations in recommender systems. In Proc. the 22nd International World Wide Web Conference, May 2013, pp.1133–1143.

  9. Agarwal D, Chen B C. fLDA: Matrix factorization through latent dirichlet allocation. In Proc. the 3rd ACM International Conference on Web Search and Data Mining, Feb. 2010, pp.91–100.

  10. McAuley J, Leskovec J. Hidden factors and hidden topics: Understanding rating dimensions with review text. In Proc. the 7th ACM Conference on Recommender Systems, Oct. 2013, pp.165–172.

  11. Wang C, Blei D. Collaborative topic modeling for recommending scientific articles. In Proc. the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2011, pp.448–456.

  12. Hofmann T. Probabilistic latent semantic indexing. In Proc. the 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Aug. 1999, pp.50–57.

  13. Breese J, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In Proc. the 14th Conference on Uncertainty in Artificial Intelligence, July 1998, pp.43–52.

  14. Cai Y, Leung H F, Li Q, Min H, Tang J, Li J. Typicalitybased collaborative filtering recommendation. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(3): 766–779.

    Article  MATH  Google Scholar 

  15. Deshpande M, Karypis G. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems, 2004, 22(1): 143–177.

    Article  MATH  Google Scholar 

  16. Liu N N, Yang Q. Eigenrank: A ranking-oriented approach to collaborative filtering. In Proc. the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2008, pp.83–90.

  17. Ma H, King I, Lyu M R. Effective missing data prediction for collaborative filtering. In Proc. the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2007, pp.39–46.

  18. Hofmann T. Collaborative filtering via Gaussian probabilistic latent semantic analysis. In Proc. the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 28-Aug. 1, 2003, pp.259–266.

  19. Si L, Jin R. Flexible mixture model for collaborative filtering. In Proc. the 20th Annual International Conference on Machine Learning, Aug. 2003, pp.704–711.

  20. Zhang Y, Koren J. Efficient Bayesian hierarchical user modeling for recommendation system. In Proc. the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2007, pp.47–54.

  21. Porteous I, Bart E, Welling M. Multi-HDP: A non parametric Bayesian model for tensor factorization. In Proc. the 23rd AAAI Conference on Artificial Intelligence, July 2008, pp.1487–1490.

  22. Salakhutdinov R, Mnih A, Hinton G. Restricted Boltzmann Machines for collaborative filtering. In Proc. the 24th Annual International Conference on Machine Learning, June 2007, pp.791–798.

  23. Salakhutdinov R, Mnih A. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In Proc. the 25th Annual International Conference on Machine Learning, July 2008, pp.880–887.

  24. Rennie J D, Srebro N. Fast maximum margin matrix factorization for collaborative prediction. In Proc. the 22nd Annual International Conference on Machine Learning, Aug. 2005, pp.713–719.

  25. Yu K, Zhu S, Lafferty J, Gong Y. Fast nonparametric matrix factorization for large-scale collaborative filtering. In Proc. the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2009, pp.211–218.

  26. Ma H, Zhou T C, Lyu M R, King I. Improving recommender systems by incorporating social contextual information. ACM Transactions on Information Systems, 2011, 29(2): Article No. 9.

  27. Wu S, Sun J, Tang J. Patent partner recommendation in enterprise social networks. In Proc. the 6th ACM International Conference on Web Search and Data Mining, Feb. 2013, pp.43–52.

  28. Jamali M, Lakshmanan L. HeteroMF: Recommendation in heterogeneous information networks using context dependent factor models. In Proc. the 22nd International World Wide Web Conference, May 2013, pp.643–654.

  29. Ma H, King I, Lyu M R. Learning to recommend with social trust ensemble. In Proc. the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2009, pp.203–210.

  30. Stern D H, Herbrich R, Graepel T. Matchbox: Large scale online Bayesian recommendations. In Proc. the 18th International World Wide Web Conference, Apr. 2009, pp.111–120.

  31. Zhang Y, Nie J. Probabilistic latent relational model for integrating heterogeneous information for recommendation. Technical Report, School of Engineering, University of California Santa Cruz, 2010.

  32. Shan H, Banerjee A. Generalized probabilistic matrix factorizations for collaborative filtering. In Proc. the 10th IEEE International Conference on Data Mining, Dec. 2010, pp.1025–1030.

  33. Ma H, Zhou D, Liu C, Lyu M R, King I. Recommender systems with social regularization. In Proc. the 4th International Conference on Web Search and Data Mining, Feb. 2011, pp.287–296.

  34. Singh A P, Gordon G J. Relational learning via collective matrix factorization. In Proc. the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2008, pp.650–658.

  35. Zhang X, Cheng J, Yuan T, Niu B, Lu H. TopRec: Domainspecific recommendation through community topic mining in social network. In Proc. the 22nd International Conference on World Wide Web, May 2013, pp.1501–1510.

  36. Tang J, Wu S, Sun J, Su H. Cross-domain collaboration recommendation. In Proc. the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2012, pp.1285–1293.

  37. Blei D M, McAuliffe J D. Supervised topic models. In Proc. the 21st Annual Conference on Neural Information Processing Systems, Dec. 2007, pp.121–128.

  38. Blei DM, Ng A Y, Jordan M I. Latent dirichlet allocation. Journal of Machine Learning Research, 2003, 3: 993–1022.

    MATH  Google Scholar 

  39. Bertsekas D. Nonlinear Programming (2nd edition). Athena Scientific, 1999.

  40. Liu J, Zhang F, Song X, Song Y I, Lin C Y, Hon H W. What’s in a name?: An unsupervised approach to link users across communities. In Proc. the 6th ACM International Conference on Web Search and Data Mining, Feb. 2013, pp.495–504.

  41. Yuan N J, Zhang F, Lian D, Zheng K, Yu S, Xie X. We know how you live: Exploring the spectrum of urban lifestyles. In Proc. the 1st Conference on Online Social Networks, Oct. 2013, pp.3–14.

  42. Ma H, King I, Lyu M R. Learning to recommend with explicit and implicit social relations. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): Article No. 29.

  43. Jamali M, Ester M. A matrix factorization technique with trust propagation for recommendation in social networks. In Proc. the 4th ACM Conference on Recommender Systems, Sept. 2010, pp.135–142.

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Authors and Affiliations

  1. Beijing Engineering Research Center of High Volume Language Information Processing & Cloud Computing, School of Computer Science, Beijing Institute of Technology, Beijing, 100081, China

    Xin Xin, Xiao-Chi Wei & He-Yan Huang

  2. Microsoft Research Asia, Beijing, 100080, China

    Chin-Yew Lin

Authors
  1. Xin Xin
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  2. Chin-Yew Lin
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  3. Xiao-Chi Wei
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  4. He-Yan Huang
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Corresponding author

Correspondence to Xin Xin.

Additional information

This work was supported by the National Basic Research 973 Program of China under Grant No. 2013CB329605, the National Natural Science Foundation of China under Grant Nos. 61300076 and 61375045, the Ph.D. Programs Foundation of Ministry of Education of China under Grant No. 20131101120035, and the Excellent Young Scholars Research Fund of Beijing Institute of Technology.

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Xin, X., Lin, CY., Wei, XC. et al. When Factorization Meets Heterogeneous Latent Topics: An Interpretable Cross-Site Recommendation Framework. J. Comput. Sci. Technol. 30, 917–932 (2015). https://doi.org/10.1007/s11390-015-1570-x

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  • DOI: https://doi.org/10.1007/s11390-015-1570-x

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