Advertisement

Data Mining and Knowledge Discovery

, Volume 31, Issue 4, pp 1031–1059 | Cite as

Enhancing social collaborative filtering through the application of non-negative matrix factorization and exponential random graph models

  • Georgios AlexandridisEmail author
  • Georgios Siolas
  • Andreas Stafylopatis
Article

Abstract

Social collaborative filtering recommender systems extend the traditional user-to-item interaction with explicit user-to-user relationships, thereby allowing for a wider exploration of correlations among users and items, that potentially lead to better recommendations. A number of methods have been proposed in the direction of exploring the social network, either locally (i.e. the vicinity of each user) or globally. In this paper, we propose a novel methodology for collaborative filtering social recommendation that tries to combine the merits of both the aforementioned approaches, based on the soft-clustering of the Friend-of-a-Friend (FoaF) network of each user. This task is accomplished by the non-negative factorization of the adjacency matrix of the FoaF graph, while the edge-centric logic of the factorization algorithm is ameliorated by incorporating more general structural properties of the graph, such as the number of edges and stars, through the introduction of the exponential random graph models. The preliminary results obtained reveal the potential of this idea.

Keywords

Social collaborative filtering Non-negative matrix factorization Exponential random graph models Recommender systems 

References

  1. Adomavicius G, Tuzhilin A (2005) 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. doi: 10.1109/TKDE.2005.99 CrossRefGoogle Scholar
  2. Alexandridis G, Siolas G, Stafylopatis A (2013) Improving social recommendations by applying a personalized item clustering policy. In: Proceedings of the fifth ACM RecSys workshop on recommender systems and the social web co-located with the 7th ACM conference on recommender systems (RecSys 2013), Hong Kong, China, 13 Oct 2013. http://ceur-ws.org/Vol-1066/Paper1
  3. Alexandridis G, Siolas G, Stafylopatis A (2015) Accuracy versus novelty and diversity in recommender systems: a nonuniform random walk approach. In: Ulusoy O, Tansel AU, Arkun E (eds) Recommendation and search in social networks, lecture notes in social networks. Springer, Berlin, pp 41–57. doi: 10.1007/978-3-319-14379-8_3 CrossRefGoogle Scholar
  4. Alpaydin E (2014) Introduction to machine learning, third, 3rd edn. MIT Press, CambridgezbMATHGoogle Scholar
  5. Bellogin A, Cantador I, Diez F, Castells Chavarriaga E (2011) An empirical comparison of social, collaborative filtering, and hybrid recommenders. ACM TIST 4:14Google Scholar
  6. Bennett J, Lanning S (2007) The netflix prize. In: Proceedings of the KDD Cup Workshop 2007, ACM, New York, pp 3–6. http://www.cs.uic.edu/~liub/KDD-cup-2007/NetflixPrize-description
  7. Cantador I, Brusilovsky P, Kuflik T (2011) 2nd workshop on information heterogeneity and fusion in recommender systems (hetrec 2011). In: Proceedings of the 5th ACM conference on recommender systems, ACM, New York, RecSys 2011Google Scholar
  8. de Wit JJ (2008) Evaluating recommender systems—an evaluation framework to predict user satisfaction for recommender systems in an electronic programme guide context. Master’s thesis, University of TwenteGoogle Scholar
  9. Desrosiers C, Karypis G (2011) A comprehensive survey of neighborhood-based recommendation methods. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender systems handbook. Springer, New York, pp 107–144. doi: 10.1007/978-0-387-85820-3_4 CrossRefGoogle Scholar
  10. Golbeck JA (2005) Computing and applying trust in web-based social networks. PhD thesis, College Park, aAI3178583Google Scholar
  11. Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets. In: Proceedings of the 2008 Eighth IEEE international conference on data mining, IEEE Computer Society, Washington, ICDM ’08, pp 263–272. doi: 10.1109/ICDM.2008.22
  12. Hubbard J (1959) Calculation of partition functions. Phys Rev Lett 3:77–78. doi: 10.1103/PhysRevLett.3.77 CrossRefGoogle Scholar
  13. Jamali M (2010) The flixster dataset. http://www.cs.sfu.ca/~sja25/personal/datasets/
  14. Jamali M, Ester M (2009) Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, New York, KDD ’09, pp 397–406. doi: 10.1145/1557019.1557067
  15. Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the Fourth ACM conference on recommender systems, ACM, New York, RecSys ’10, pp 135–142. doi: 10.1145/1864708.1864736
  16. Konstas I, Stathopoulos V, Jose JM (2009) On social networks and collaborative recommendation. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval, ACM, New York, SIGIR ’09, pp 195–202. doi: 10.1145/1571941.1571977
  17. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, KDD ’08, pp 426–434. doi: 10.1145/1401890.1401944
  18. Lee D, Seung H (1999) Learning the parts of objects by non-negative matrix factorization. Nature. http://www.nature.com/nature/journal/v401/n6755/abs/401788a0.html
  19. Lee DD, Seung HS (2000) Algorithms for non-negative matrix factorization. In: In NIPS, MIT Press, pp 556–562Google Scholar
  20. Ma H, King I, Lyu MR (2009) Learning to recommend with social trust ensemble. In: Proceedings of the 32nd International ACM SIGIR conference on research and development in information retrieval, ACM, New York, SIGIR ’09, pp 203–210. doi: 10.1145/1571941.1571978
  21. Massa P, Avesani P (2009) Trust metrics in recommender systems. In: Golbeck J (ed) Computing with social trust, human computer interaction series. Springer, London, pp 259–285. doi: 10.1007/978-1-84800-356-9_10 CrossRefGoogle Scholar
  22. Newman MEJ (2010) Networks: an introduction, 1st edn. Oxford University Press, OxfordCrossRefGoogle Scholar
  23. Nunez-Gonzalez JD, Grana M, Apolloni B (2015) Reputation features for trust prediction in social networks. Neurocomputing 166:1–7. doi: 10.1016/j.neucom.2014.10.099 CrossRefGoogle Scholar
  24. Park J, Newman M (2004a) Solution of the two-star model of a network. Phys Rev E 70(066):146. doi: 10.1103/PhysRevE.70.066146 MathSciNetCrossRefGoogle Scholar
  25. Park J, Newman M (2004b) Statistical mechanics of networks. Phys Rev E 70(6):066117. doi: 10.1103/PhysRevE.70.066117 cond-mat/0405566MathSciNetCrossRefGoogle Scholar
  26. Park J, Newman MEJ (2005) Solution for the properties of a clustered network. Phys Rev E 72(2):026136. doi: 10.1103/PhysRevE.72.026136 cond-mat/0412579CrossRefGoogle Scholar
  27. Psorakis I, Roberts S, Ebden M, Sheldon B (2011) Overlapping community detection using bayesian non-negative matrix factorization. Phys Rev E 83(066):114. doi: 10.1103/PhysRevE.83.066114 CrossRefGoogle Scholar
  28. Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM conference on computer supported cooperative work, ACM, New York, CSCW ’94, pp 175–186. doi: 10.1145/192844.192905
  29. Robins G, Pattison P, Kalish Y, Lusher D (2007) An introduction to exponential random graph (p*) models for social networks. Soc Netw 29(2):173–191. doi: 10.1016/j.socnet.2006.08.002 (special section: advances in exponential random graph (p*) models)CrossRefGoogle Scholar
  30. Shi Y, Larson M, Hanjalic A (2010) List-wise learning to rank with matrix factorization for collaborative filtering. In: Proceedings of the fourth ACM conference on recommender systems, ACM, New York, RecSys ’10, pp 269–272. doi: 10.1145/1864708.1864764
  31. Wang YX, Zhang YJ (2013) Nonnegative matrix factorization: a comprehensive review. IEEE Trans Knowl Data Eng 25(6):1336–1353. doi: 10.1109/TKDE.2012.51 CrossRefGoogle Scholar
  32. Weimer M, Karatzoglou A, Le QV, Smola A (2007) Cofirank maximum margin matrix factorization for collaborative ranking. In: Proceedings of the 20th international conference on neural information processing systems, Curran Associates Inc., USA, NIPS’07, pp 1593–1600. http://dl.acm.org/citation.cfm?id=2981562.2981762
  33. Yang X, Steck H, Guo Y, Liu Y (2012) On top-k recommendation using social networks. In: Proceedings of the sixth ACM conference on recommender systems, ACM, New York, RecSys ’12, pp 67–74. doi: 10.1145/2365952.2365969
  34. Yang X, Guo Y, Liu Y, Steck H (2014) A survey of collaborative filtering based social recommender systems. Comput Commun 41:1–10. doi: 10.1016/j.comcom.2013.06.009 CrossRefGoogle Scholar
  35. Zhou Y, Wilkinson D, Schreiber R, Pan R (2008) Large-scale parallel collaborative filtering for the netflix prize. In: Proceedings of the 4th international conference on algorithmic aspects in information and management, Springer-Verlag, Berlin, Heidelberg, AAIM ’08, pp 337–348. doi: 10.1007/978-3-540-68880-8_32

Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.School of Electrical and Computer EngineeringNational Technical University of AthensZografouGreece

Personalised recommendations