Personalized entity recommendation: a heterogeneous information network approach

ABSTRACT

Among different hybrid recommendation techniques, network-based entity recommendation methods, which utilize user or item relationship information, are beginning to attract increasing attention recently. Most of the previous studies in this category only consider a single relationship type, such as friendships in a social network. In many scenarios, the entity recommendation problem exists in a heterogeneous information network environment. Different types of relationships can be potentially used to improve the recommendation quality. In this paper, we study the entity recommendation problem in heterogeneous information networks. Specifically, we propose to combine heterogeneous relationship information for each user differently and aim to provide high-quality personalized recommendation results using user implicit feedback data and personalized recommendation models.

In order to take full advantage of the relationship heterogeneity in information networks, we first introduce meta-path-based latent features to represent the connectivity between users and items along different types of paths. We then define recommendation models at both global and personalized levels and use Bayesian ranking optimization techniques to estimate the proposed models. Empirical studies show that our approaches outperform several widely employed or the state-of-the-art entity recommendation techniques.

References

1. J. Basilico and T. Hofmann. Unifying collaborative and content-based filtering. In ICML, 2004.
2. L. Bottou. Large-scale machine learning with stochastic gradient descent. In COMPSTAT. 2010.
3. R. H. Byrd, P. Lu, J. Nocedal, and C. Zhu. A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing, 16(5):1190--1208, 1995.
4. S. Chakrabarti. Dynamic personalized pagerank in entity-relation graphs. In WWW, 2007.
5. C. H. Ding, T. Li, and M. I. Jordan. Convex and semi-nonnegative matrix factorizations. PAMI, 2010.
6. Q. Gu, J. Zhou, and C. Ding. Collaborative filtering: Weighted nonnegative matrix factorization incorporating user and item graphs. In SDM, 2010.
7. I. Guy, N. Zwerdling, D. Carmel, I. Ronen, E. Uziel, S. Yogev, and S. Ofek-Koifman. Personalized recommendation of social software items based on social relations. In RecSys, 2009.
8. T. Hofmann. Collaborative filtering via gaussian probabilistic latent semantic analysis. In SIGIR, 2003.
9. Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In ICDM, 2008.
10. M. Jamali and M. Ester. A matrix factorization technique with trust propagation for recommendation in social networks. In SIGKDD, 2010.
11. G. Jeh and J. Widom. Simrank: a measure of structural-context similarity. In SIGKDD, 2002.
12. M. Ji, J. Han, and M. Danilevsky. Ranking-based classification of heterogeneous information networks. In SIGKDD, 2011.
13. T. Joachims. Training linear svms in linear time. In SIGKDD, 2006.
14. Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In SIGKDD, 2008.
15. Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30--37, 2009.
16. T.-T. Kuo, R. Yan, Y.-Y. Huang, P.-H. Kung, and S.-D. Lin. Unsupervised link prediction using aggregative statistics on heterogeneous social networks. In SIGKDD. ACM, 2013.
17. N. Lao and W. Cohen. Relational retrieval using a combination of path-constrained random walks. Machine learning, 2010.
18. H. Ma, I. King, and M. R. Lyu. Learning to recommend with social trust ensemble. In SIGIR, 2009.
19. H. Ma, H. Yang, M. Lyu, and I. King. Sorec: social recommendation using probabilistic matrix factorization. In CIKM, 2008.
20. P. Melville, R. J. Mooney, and R. Nagarajan. Content-boosted collaborative filtering for improved recommendations. In AAAI, 2002.
21. S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In UAI, 2009.
22. J. D. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. In ICML, 2005.
23. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW, 2001.
24. Y. Sun, J. Han, X. Yan, S. P. Yu, and T. Wu. PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks. In VLDB, 2011.
25. Y. Sun, B. Norick, J. Han, X. Yan, P. S. Yu, and X. Yu. Integrating meta-path selection with user guided object clustering in heterogeneous information networks. In KDD, 2012.
26. Y. Sun, Y. Yu, and J. Han. Ranking-based clustering of heterogeneous information networks with star network schema. In KDD, 2009.
27. M. Szell, R. Lambiotte, and S. Thurner. Multirelational organization of large-scale social networks in an online world. Proceedings of the National Academy of Sciences, 107(31):13636--13641, 2010.
28. X. Yu, Q. Gu, M. Zhou, and J. Han. Citation prediction in heterogeneous bibliographic networks. In SDM, 2012.
29. X. Yu, X. Ren, Q. Gu, Y. Sun, and J. Han. Collaborative filtering with entity similarity regularization in heterogeneous information networks. In IJCAI HINA, 2013.
30. X. Yu, X. Ren, Y. Sun, B. Sturt, U. Khandelwal, Q. Gu, B. Norick, and J. Han. Entity recommendation in heterogeneous information networks with implicit user feedback. In RecSys, 2013.
31. Q. Yuan, L. Chen, and S. Zhao. Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation. In RecSys, 2011.

Index Terms

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Document filtering

      • Information extraction