On Weighted Hybrid Track Recommendations

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7977)


Music is a highly subjective domain, which makes it a challenging research area for recommender systems. In this paper, we present our TRecS (Track Recommender System) prototype, a hybrid recommender that blends three different recommender techniques into one score. Since traceability is an important issue for the acceptance of recommender systems by users, we have implemented a detailed explanation feature that supports transparency about the contribution of each sub-recommender for the overall result. To avoid overspecialization, TRecS peppers the result list with recommendations that are based on a serendipity metric. This way, users can benefit from both recommendations aligned with their current taste while gaining some diversification.


Recommender System Cosine Similarity Typical Song Result List Recommendation List 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Zhang, Y.C., Séaghdha, D.Ó., Quercia, D., Jambor, T.: Auralist: Introducing Serendipity Into Music Recommendation. In: Adar, E., Teevan, J., Agichtein, E., Maarek, Y. (eds.) WSDM, pp. 13–22. ACM (2012)Google Scholar
  2. 2.
    Burke, R.: Hybrid Web Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    Baeza-Yates, R.A., Ribeiro-Neto, B.A.: Modern Information Retrieval - The Concepts and Technology Behind Search, 2nd edn. Pearson Education Ltd., Harlow (2011)Google Scholar
  5. 5.
    Ziegler, C.N., McNee, S., Konstan, J., Lausen, G.: Improving Recommendation Lists Through Topic Diversification. In: Proceedings of the 14th International World Wide Web Conference, Chiba, Japan. ACM Press (May 2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Computer ScienceAlbert-Ludwigs-Universität FreiburgGermany
  2. 2.American Express, PAYBACK GmbHMünchenGermany

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