期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 55, 期 -, 页码 546-558出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2016.02.020
关键词
Music recommender system; Interactive radio network; Hybrid recommender system; Information fusion; Adaptive tag-aware profiling; Implicit feedback
类别
资金
- Russian Foundation for Basic Research [13-07-00504]
- Basic Research Program of Higher School of Economics
We present a new recommender system developed for the Russian interactive radio network FMhost. To the best of our knowledge, it is the first model and associated case study for recommending radio stations hosted by real DJs rather than automatically built streamed playlists. To address such problems as cold start, gray sheep, boosting of rankings, preference and repertoire dynamics, and absence of explicit feedback, the underlying model combines a collaborative user-based approach with personalized information from tags of listened tracks in order to match user and radio station profiles. This is made possible with adaptive tag-aware profiling that follows an online learning strategy based on user history. We compare the proposed algorithms with singular value decomposition (SVD) in terms of precision, recall, and normalized discounted cumulative gain (NDCG) measures; experiments show that in our case the fusion based approach demonstrates the best results. In addition, we give a theoretical analysis of some useful properties of fusion-based linear combination methods in terms of graded ordered sets. (C) 2016 Elsevier Ltd. All rights reserved.
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