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Joint Factorizational Topic Models for Cross-City Recommendation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10366)

Abstract

The research of personalized recommendation techniques today has mostly parted into two mainstream directions, namely, the factorization-based approaches and topic models. Practically, they aim to benefit from the numerical ratings and textual reviews, correspondingly, which compose two major information sources in various real-world systems, including Amazon, Yelp, eBay, Netflix, and many others.

However, although the two approaches are supposed to be correlated for their same goal of accurate recommendation, there still lacks a clear theoretical understanding of how their objective functions can be mathematically bridged to leverage the numerical ratings and textual reviews collectively, and why such a bridge is intuitively reasonable to match up their learning procedures for the rating prediction and top-N recommendation tasks, respectively.

In this work, we exposit with mathematical analysis that, the vector-level randomization functions to harmonize the optimization objectives of factorizational and topic models unfortunately do not exist at all, although they are usually pre-assumed and intuitively designed in the literature.

Fortunately, we also point out that one can simply avoid the seeking of such a randomization function by optimizing a Joint Factorizational Topic (JFT) model directly. We further apply our JFT model to the cross-city Point of Interest (POI) recommendation tasks for performance validation, which is an extremely difficult task for its inherent cold-start nature. Experimental results on real-world datasets verified the appealing performance of our approach against previous methods with pre-assumed randomization functions in terms of both rating prediction and top-N recommendation tasks.

Notes

Acknowledgement

We thank the reviewers for their valuable suggestions. This work is supported by Natural Science Foundation of China (Grant Nos. 61532011, 61672311) and National Key Basic Research Program (2015CB358700).

References

  1. 1.
    Aciar, S., Zhang, D., Simoff, S., Debenham, J.: Informed recommender: basing recommendations on consumer product reviews. Intell. Syst. 22(3), 39–47 (2007)CrossRefGoogle Scholar
  2. 2.
    Agarwal, D., Chen, B.C.: fLDA: matrix factorization through latent Dirichlet allocation. In: WSDM (2010)Google Scholar
  3. 3.
    Baatarjav, E.-A., Phithakkitnukoon, S., Dantu, R.: Group recommendation system for Facebook. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2008. LNCS, vol. 5333, pp. 211–219. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-88875-8_41 CrossRefGoogle Scholar
  4. 4.
    Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)CrossRefGoogle Scholar
  5. 5.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. JMLR 2003(3), 993–1022 (2003)MATHGoogle Scholar
  6. 6.
    Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-N recommendation tasks. In: RecSys pp. 39–46 (2010)Google Scholar
  7. 7.
    Das, A., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: WWW pp. 271–280 (2007)Google Scholar
  8. 8.
    Davidson, J., Liebald, B., Liu, J., et al.: The YouTube video recommendation system. In: RecSys, pp. 293–296 (2010)Google Scholar
  9. 9.
    Ganu, G., Elhadad, N., Marian, A.: Beyond the stars: improving rating predictions using review text content. In: WebDB (2009)Google Scholar
  10. 10.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM (2008)Google Scholar
  11. 11.
    Jakob, N., Weber, S.H., Müller, M.C., et al.: Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations. In: TSA (2009)Google Scholar
  12. 12.
    Knott, M., Bartholomew, D.: Latent Variable Models and Factor Analysis. Kendall’s Library of Statistics 2 (1999)Google Scholar
  13. 13.
    Ko, M., Kim, H.W., Yi, M.Y., Song, J., Liu, Y.: MovieCommenter: aspect-based collaborative filtering by utilizing user comments. In: CollaborateCom (2011)Google Scholar
  14. 14.
    Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User-Adap. Interact. 22(1–2), 101–123 (2012)CrossRefGoogle Scholar
  15. 15.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42, 8 (2009)CrossRefGoogle Scholar
  16. 16.
    Koren, Y., Bell, R.: Advances in Collaborative Filtering. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 145–186. Springer, Heidelberg (2011). doi: 10.1007/978-0-387-85820-3_5 CrossRefGoogle Scholar
  17. 17.
    Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Proceedings of NIPS (2001)Google Scholar
  18. 18.
    Lika, B., Kolomvatsos, K., Hadjiefthymiades, S.: Facing the cold start problem in recommender systems. Expert Syst. Appl. 41(4), 2065–2073 (2014)CrossRefGoogle Scholar
  19. 19.
    Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. Internet Comput. 7(1), 76–80 (2003)CrossRefGoogle Scholar
  20. 20.
    Ling, G., Lyu, M.R., King, I.: Ratings meet reviews: a combined approach to recommend. In: RecSys (2014)Google Scholar
  21. 21.
    McAuley, J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In: RecSys, pp. 165–172 (2013)Google Scholar
  22. 22.
    Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-72079-9_10 CrossRefGoogle Scholar
  23. 23.
    Purushotham, S., Liu, Y., Kuo, C.C.J.: collaborative topic regression with social matrix factorization for recommendation systems. In: ICML (2012)Google Scholar
  24. 24.
    Rendle, S., Freudenthaler, C., Gantner, Z., Thieme, L.S.: BPR: Bayesian Personalized Ranking from implicit feedback. In: UAI (2009)Google Scholar
  25. 25.
    Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. Springer, Heidelberg (2011)CrossRefMATHGoogle Scholar
  26. 26.
    Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of ICML (2008)Google Scholar
  27. 27.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Proceedings of NIPS (2008)Google Scholar
  28. 28.
    Srebro, N., Rennie, J.D.M., Jaakkola, T.S.: Maximum-margin matrix factorization. In: NIPS (2005)Google Scholar
  29. 29.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. In: Advances in AI, p. 4 (2009)Google Scholar
  30. 30.
    Takacs, G., Pilaszy, I., Nemeth, B., Tikk, D.: Investigation of various matrix factorization methods for large recommender systems. In: Proceedings of ICDM (2008)Google Scholar
  31. 31.
    Terzi, M., Ferrario, M.A., Whittle, J.: Free text in user reviews: their role in recommender systems. In: RecSys (2011)Google Scholar
  32. 32.
    Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: KDD (2011)Google Scholar
  33. 33.
    Xu, X., Datta, A., Dutta, K.: Using adjective features from user reviews to generate higher quality and explainable recommendations. IFIP Advances in Info. and Com. Tech. 389, 18–34 (2012)Google Scholar
  34. 34.
    Zhang, Y., Lai, G., Zhang, M., Zhang, Y., Liu, Y., Ma, S.: Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In: SIGIR (2014)Google Scholar
  35. 35.
    Zhang, Y., Zhang, H., Zhang, M., Liu, Y., et al.: Do users rate or review? boost phrase-level sentiment labeling with review-level sentiment classification. In: SIGIR (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Interdisciplinary Information SciencesTsinghua UniversityBeijingChina
  2. 2.Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  3. 3.College of Information and Computer ScienceUniversity of Massachusetts AmherstAmherstUSA

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