Chapter

Web Engineering

Volume 7977 of the series Lecture Notes in Computer Science pp 486-489

On Weighted Hybrid Track Recommendations

  • Simon FranzAffiliated withInstitute of Computer Science, Albert-Ludwigs-Universität Freiburg
  • , Thomas HornungAffiliated withInstitute of Computer Science, Albert-Ludwigs-Universität Freiburg
  • , Cai-Nicolas ZieglerAffiliated withAmerican Express, PAYBACK GmbH
  • , Martin Przyjaciel-ZablockiAffiliated withInstitute of Computer Science, Albert-Ludwigs-Universität Freiburg
  • , Alexander SchätzleAffiliated withInstitute of Computer Science, Albert-Ludwigs-Universität Freiburg
  • , Georg LausenAffiliated withInstitute of Computer Science, Albert-Ludwigs-Universität Freiburg

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Abstract

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.