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Space efficiency in group recommendation

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Abstract

Imagine a system that gives you satisfying recommendations when you want to rent a movie with friends or find a restaurant to celebrate a colleague’s farewell: at the core of such a system is what we call group recommendation. While computing individual recommendations have received lots of attention (e.g., Netflix prize), group recommendation has been confined to studying users’ satisfaction with different aggregation strategies. In this paper (Some results are published in an earlier conference paper (Amer-Yahia et al. in VLDB, 2009). See Sect. “Paper contributions and outline” for details.), we describe the challenges and desiderata of group recommendation and formalize different group consensus semantics that account for both an item’s predicted ratings to the group members and the disagreements among them. We focus on the design and implementation of efficient group recommendation algorithms that intelligently prune and merge per-user predicted rating lists and pairwise disagreement lists of items. We further explore the impact of space constraints on maintaining per-user and pairwise item lists and develop two complementary solutions that leverage shared user behavior to maintain the efficiency of our recommendation algorithms within a space budget. The first solution, behavior factoring, factors out user agreements from disagreement lists, while the second solution, partial materialization, selectively materializes a subset of disagreement lists. Finally, we demonstrate the usefulness of our group recommendations and the efficiency and scalability of our algorithms using an extensive set of experiments on the 10 M ratings MovieLens data set.

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References

  1. Adomavicius G., Tuzhilin A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

    Article  Google Scholar 

  2. Amer-Yahia, S., Benedikt, M., Lakshmanan, L., Stoyanovich, J.: Efficient network-aware search in collaborative tagging sites. In: VLDB (2008)

  3. Amer-Yahia, S., Roy, S.B., Chawla, A., Das, G., Yu, C.: Group recommendation: Semantics and efficiency. In: VLDB (2009)

  4. Baatarjav E.-A., Phithakkitnukoon S., Dantu R.: Group recommendation system for facebook. In: Meersman, R., Tari, Z., Herrero, P. (eds) OTM Workshops, vol. 5333 of Lecture Notes in Computer Science., pp. 211–219. Springer, Berlin (2008)

    Google Scholar 

  5. Carey, M.J., Kossmann, D.: On saying “enough already!” in sql. In: SIGMOD (1997)

  6. Chen Y.-L., Cheng L.-C., Chuang C.-N.: A group recommendation system with consideration of interactions among group members. Expert Syst. Appl. 34(3), 2082–2090 (2008)

    Article  Google Scholar 

  7. Fagin R.: Combining fuzzy information: An overview. SIGMOD Rec. 32(2), 109–118 (2002)

    Article  Google Scholar 

  8. Fagin, R. et al.: Optimal aggregation algorithms for middleware. In: PODS (2001)

  9. Garey M.R., Johnson D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Company, San Francisco (1979)

    MATH  Google Scholar 

  10. GroupLens at University of Minnesota. http://www.grouplens.org/node/73

  11. Jameson, A., Smyth, B.: The Adaptive Web, vol. 4321 of LNCS, Chapter Recommendation to Groups, p. 596. Springer, Berlin (2007)

  12. Jarvelin K., Kekalainen K.: Cumulated gain-based evaluation of ir techniques. ACM TOIS 20(4), 422–446 (2002)

    Article  Google Scholar 

  13. Kim, H.O.J.K., Kim, H.K., Ryu, Y.: A group recommendation system for online communities. Int. J. Inf. Manage. (2009)

  14. Konstan, J.A.: Introduction to recommender systems. In: SIGIR (2007)

  15. Marian, A., Amer-Yahia, S., Koudas, N., Srivastava, D.: Adaptive processing of top-k queries in xml. In: ICDE (2005)

  16. Martello S., Toth P.: Knapsack Problems: Algorithms and Computer Implementations (Wiley-Interscience Series in Discrete Mathematics and Optimization). Wiley, London (1990)

    Google Scholar 

  17. McCarthy K., McGinty L., Smyth B.: Case-based group recommendation: Compromising for success. In: Weber, R., Richter, M.M. (eds) ICCBR, vol. 4626 of Lecture Notes in Computer Science, pp. 299–313. Springer, Berlin (2007)

    Google Scholar 

  18. O’Connor, M., Cosley, D., Konstan, J.A., Riedl, J.: Polylens: A Recommender System for Groups of User. In: ECSCW, pp. 199–218. (2001)

  19. Park M.-H., Park H.-S., Cho S.-B.: Restaurant recommendation for group of people in mobile environments using probabilistic multi-criteria decision making. In: Lee, S., Choo, H., Ha, S., Shin, I.C. (eds) APCHI, vol 5068 of Lecture Notes in Computer Science, pp. 114–122. Springer, Berlin (2008)

    Google Scholar 

  20. Sahni, S.: Approximate algorithms for the 01 knapsack problem. In: Journal of the ACM. ACM (1975)

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Authors and Affiliations

  1. University of Texas at Arlington, Arlington, TX, USA

    Senjuti Basu Roy, Ashish Chawla & Gautam Das

  2. Yahoo! Research, Barcelona, Spain

    Sihem Amer-Yahia

  3. Google Research, New York, USA

    Cong Yu

Authors
  1. Senjuti Basu Roy
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  2. Sihem Amer-Yahia
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  3. Ashish Chawla
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  4. Gautam Das
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  5. Cong Yu
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Correspondence to Senjuti Basu Roy.

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Basu Roy, S., Amer-Yahia, S., Chawla, A. et al. Space efficiency in group recommendation. The VLDB Journal 19, 877–900 (2010). https://doi.org/10.1007/s00778-010-0209-3

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  • DOI: https://doi.org/10.1007/s00778-010-0209-3

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