Personalized Ranking Recommendation via Integrating Multiple Feedbacks

  • Jian Liu
  • Chuan Shi
  • Binbin Hu
  • Shenghua Liu
  • Philip S. Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10235)

Abstract

Recently, recommender system has attracted a lot of attentions, which helps users to find items of interest through utilizing the user-item interaction information and/or content information associated with users and items. The interaction information (i.e., feedback) between users and items are widely exploited to build recommendation models. The feedback data in recommender systems usually comes in the form of both explicit feedback (e.g., rating) and implicit feedback (e.g., browsing histories, click logs). Although existing works have begun to utilize either explicit or implicit feedback for better recommendation, they did not make best use of these feedback information together. In this paper, we first study the personalized ranking recommendation problem by integrating multiple feedbacks, i.e., one type of explicit feedback and multiple types of implicit feedbacks. Then we propose a unified and flexible personalized ranking framework MFPR to integrate multiple feedbacks. Moreover, as there are no readily available training data, an explicit feedback based training data generation algorithm is designed to generate item pairs with more accurate partial order consistent with the multiple feedbacks for the proposed ranking model. Extensive experiments on two real-world datasets validate the effectiveness of the MFPR model, and the integration of multiple feedbacks making up better complementary information significantly improves recommendation performance.

Keywords

Recommender system Multiple feedbacks Explicit feedback Implicit feedback Bayesian Personalized Ranking 

References

  1. 1.
    Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.: Learning to rank using gradient descent. In: ICML, pp. 89–96. ACM (2005)Google Scholar
  2. 2.
    da Costa, A.F., Manzato, M.G.: Ensemble learning in recommender systems: combining multiple user interactions for ranking personalization. In: PBSMW, pp. 47–54. ACM (2014)Google Scholar
  3. 3.
    Gantner, Z., Rendle, S., et al.: Mymedialite: a free recommender system library. In: RecSys, pp. 305–308. ACM (2011)Google Scholar
  4. 4.
    He, R., McAuley, J.: VBPR: visual Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1510.01784 (2015)
  5. 5.
    Hoyer, P.O.: Non-negative matrix factorization with sparseness constraints. JMLR 5, 1457–1469 (2004)MathSciNetMATHGoogle Scholar
  6. 6.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM, pp. 263–272. IEEE (2008)Google Scholar
  7. 7.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: SIGKDD, pp. 426–434. ACM (2008)Google Scholar
  8. 8.
    Lee, J., Bengio, S., et al.: Local collaborative ranking. In: WWW, pp. 85–96. ACM (2014)Google Scholar
  9. 9.
    Oard, D.W., Kim, J., et al.: Implicit feedback for recommender systems. In: AAAI, pp. 81–83 (1998)Google Scholar
  10. 10.
    Pan, R., Zhou, Y., et al.: One-class collaborative filtering. In: ICDM, pp. 502–511. IEEE (2008)Google Scholar
  11. 11.
    Rendle, S.: Factorization machines. In: ICDM, pp. 995–1000. IEEE (2010)Google Scholar
  12. 12.
    Rendle, S.: Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. 3(3), 57:1–57:22 (2012)CrossRefGoogle Scholar
  13. 13.
    Rendle, S., Freudenthaler, C., et al.: BPR: Bayesian personalized ranking from implicit feedback. In: UAI, pp. 452–461. AUAI Press (2009)Google Scholar
  14. 14.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. Citeseer (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jian Liu
    • 1
  • Chuan Shi
    • 1
    • 4
  • Binbin Hu
    • 1
  • Shenghua Liu
    • 2
  • Philip S. Yu
    • 3
  1. 1.Beijing Key Lab of Intelligent Telecommunications Software and MultimediaBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Chinese Academy of SciencesInstitute of Computing TechnologyBeijingChina
  3. 3.University of Illinois at ChicagoChicagoUSA
  4. 4.Beijing Advanced Innovation Center for Imaging TechnologyCapital Normal UniversityBeijingChina

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