Advertisement

Social Influence-Based Similarity Measures for User-User Collaborative Filtering Applied to Music Recommendation

  • Diego Sánchez-Moreno
  • Javier Pérez-Marcos
  • Ana B. Gil González
  • Vivian López Batista
  • María N. Moreno-GarcíaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 801)

Abstract

Social characteristics present in current music streaming services allow to use methods for endowing these systems with more reliable recommendation functionalities. There are many proposals in the literature that take advantage of that information and use it in the context of recommender systems. However, in the specific application domain of music the studies are much more limited, and the methods developed for other domains cannot be often applied since they require social interaction data that are not available in the streaming systems. In this paper, we present a method to determine social influence of users uniquely from friendship relations. The degree of influence obtained is used to define new similarity metrics for collaborative filtering (CF) where more weight is given to more influential users.

Keywords

Music recommender systems Social influence Trust Collaborative filtering Streaming services 

References

  1. 1.
    Akcora, C.G., Carminati, B., Ferrari, E.: User similarities on social networks. Soc. Netw. Anal. Min. 3(3), 475–495 (2013)CrossRefGoogle Scholar
  2. 2.
    Breese, J.S., Heckerman, D., Kadie C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, pp. 43–52 (1998)Google Scholar
  3. 3.
    Cantador, I., Brusilovsky, P., Kuflik, T.: 2nd workshop on information heterogeneity and fusion in recommender systems (HetRec 2011). In: Proceedings of the 5th ACM Conference on Recommender Systems, RecSys 2011, New York, NY, USA. ACM (2011)Google Scholar
  4. 4.
    Kalaï, A., Abdelghani, W., Zayani, C.A., Amous, I.: LoTrust: a social trust level model based on time-aware social interactions and interests similarity. In: 14th IEEE Fourteenth Annual Conference on Privacy, Security and Trust, Auckland, NewZeland, pp. 428–436 (2016)Google Scholar
  5. 5.
    Lee, K., Lee, K.: Escaping your comfort zone: a graph-based recommender system for finding novel recommendations among relevant items. Expert Syst. Appl. 42(2015), 4851–4858 (2015)CrossRefGoogle Scholar
  6. 6.
    Massa, P., Avesani, P.: Trust–aware recommender systems. In: ACM Conference on Recommender Systems, RecSys, Minneapolis, MN, USA, pp. 17–24 (2007)Google Scholar
  7. 7.
    Pacula, M.: A matrix factorization algorithm for music recommendation using implicit user feedback. http://www.mpacula.com/publications/lastfm.pdf
  8. 8.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithm. In: Proceedings of the Tenth International World Wide Web Conference, pp. 285–295 (2001)Google Scholar
  9. 9.
    Shardanand, U., Maes, P.: Social information filtering: algorithms for automating ‘Word of Mouth’. In: Proceedings of the Conference on Human Factors in Computing Systems (CHI 1995), Denver, pp. 210–217 (1995)Google Scholar
  10. 10.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 1–19 (2009)CrossRefGoogle Scholar
  11. 11.
    Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems RecSys 2011, New York, NY, USA, pp. 109–116. ACM (2011)Google Scholar
  12. 12.
    Yuan, T., Cheng, J., Zhang, X., Liu, Q., Lu, H.: How friends affect user behaviors? An exploration of social relation analysis for recommendation. Knowl.-Based Syst. 88, 70–84 (2015)CrossRefGoogle Scholar
  13. 13.
    Ziegler, C., Golbeck, J.: Investigating interactions of trust and interest similarity. Decis. Support Syst. 43(2), 460–475 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Diego Sánchez-Moreno
    • 1
  • Javier Pérez-Marcos
    • 1
  • Ana B. Gil González
    • 1
  • Vivian López Batista
    • 1
  • María N. Moreno-García
    • 1
    Email author
  1. 1.Department of Computing and AutomationUniversity of SalamancaSalamancaSpain

Personalised recommendations