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Collaborative Filtering via Different Preference Structures

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Knowledge Science, Engineering and Management (KSEM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10412))

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Abstract

Recently, social network websites start to provide third-parity sign-in options via the OAuth 2.0 protocol. For example, users can login Netflix website using their Facebook accounts. By using this service, accounts of the same user are linked together, and so does their information. This fact provides an opportunity of creating more complete profiles of users, leading to improved recommender systems. However, user opinions distributed over different platforms are in different preference structures, such as ratings, rankings, pairwise comparisons, voting, etc. As existing collaborative filtering techniques assume the homogeneity of preference structure, it remains a challenge task of how to learn from different preference structures simultaneously. In this paper, we propose a fuzzy preference relation-based approach to enable collaborative filtering via different preference structures. Experiment results on public datasets demonstrate that our approach can effectively learn from different preference structures, and show strong resistance to noises and biases introduced by cross-structure preference learning.

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Notes

  1. 1.

    http://grouplens.org/datasets/movielens.

  2. 2.

    http://snap.stanford.edu/data/web-Movies.html.

  3. 3.

    http://grouplens.org/datasets/eachmovie.

References

  1. Besag, J.: Spatial interaction and the statistical analysis of lattice systems. J. Roy. Stat. Soc. 36(2), 192–236 (1974)

    MathSciNet  MATH  Google Scholar 

  2. Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O’Reilly Media Inc., Newton (2009)

    MATH  Google Scholar 

  3. Brun, A., Hamad, A., Buffet, O., Boyer, A.: Towards preference relations in recommender systems. In: PL: ECML/PKDD (2010)

    Google Scholar 

  4. Chapelle, O., Metlzer, D., Zhang, Y., Grinspan, P.: Expected reciprocal rank for graded relevance. In: CIKM 2009, pp. 621–630. ACM (2009)

    Google Scholar 

  5. Desarkar, M.S., Saxena, R., Sarkar, S.: Preference relation based matrix factorization for recommender systems. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 63–75. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31454-4_6

    Chapter  Google Scholar 

  6. Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM TOIS 20(4), 422–446 (2002)

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Liu, J., Caihua, W., Xiong, Y., Liu, W.: List-wise probabilistic matrix factorization for recommendation. Inf. Sci. 278, 434–447 (2014)

    Article  Google Scholar 

  9. Liu, S., Li, G., Tran, T., Jiang, Y.: Preference relation-based markov random fields for recommender systems. Mach. Learn. 106(4), 523–546 (2017)

    Article  MathSciNet  Google Scholar 

  10. McCullagh, P.: Regression models for ordinal data. J. Roy. Stat. Soc. B 42(2), 109–142 (1980)

    MathSciNet  MATH  Google Scholar 

  11. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: UAI 2009, pp. 452–461. AUAI Press (2009)

    Google Scholar 

  12. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: CSCW 1994, pp. 175–186. ACM (1994)

    Google Scholar 

  13. Schacter, D.L., Dodson, C.S.: Misattribution, false recognition, the sins of memory. Philos. Trans. Roy. Soc. Lond. B: Biol. Sci. 356(1413), 1385–1393 (2001)

    Article  Google Scholar 

  14. Tran, T., Phung, D.Q., Venkatesh, S.: Preference networks: probabilistic models for recommendation systems. In: AusDM 2007, pp. 195–202. ACS (2007)

    Google Scholar 

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Acknowledgment

This work was partially supported by the Guangxi Key Laboratory of Trusted Software (No. KX201528).

Author information

Authors and Affiliations

  1. University of Technology Sydney, Sydney, Australia

    Shaowu Liu & Guandong Xu

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Na Pang

  3. Arizona State University, Tempe, USA

    Huan Liu

Authors
  1. Shaowu Liu
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  2. Na Pang
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  3. Guandong Xu
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  4. Huan Liu
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Corresponding author

Correspondence to Shaowu Liu .

Editor information

Editors and Affiliations

  1. Deakin University, Burwood, Victoria, Australia

    Gang Li

  2. University of Arizona, Tucson, Arizona, USA

    Yong Ge

  3. Southwest University, Chongqing, China

    Zili Zhang

  4. Peking University, Beijing, China

    Zhi Jin

  5. University of Technology Sydney, Sydney, New South Wales, Australia

    Michael Blumenstein

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Liu, S., Pang, N., Xu, G., Liu, H. (2017). Collaborative Filtering via Different Preference Structures. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds) Knowledge Science, Engineering and Management. KSEM 2017. Lecture Notes in Computer Science(), vol 10412. Springer, Cham. https://doi.org/10.1007/978-3-319-63558-3_26

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  • DOI: https://doi.org/10.1007/978-3-319-63558-3_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63557-6

  • Online ISBN: 978-3-319-63558-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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