Improving Quality Of Service For Radio Station Hosting: An Online Recommender System Based On Information Fusion

Dmitry Ignatov (), Sergey Nikolenko (), Taimuraz Abaev and Jonas Poelmans
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Sergey Nikolenko: National Research University Higher School of Economics
Taimuraz Abaev: National Research University Higher School of Economics
Jonas Poelmans: National Research University Higher School of Economics

HSE Working papers from National Research University Higher School of Economics

Abstract: We present a new recommender system developed for the Russian interactive radio network FMhost. The system aims to improve the quality of this service; it is designed specifically to deal with small datasets, overcoming the shortage of data on observed user behavior. The underlying model combines a collaborative user-based approach with information from tags of listened tracks in order to match user and radio station profiles. It follows an adaptive online learning strategy based on both user history and implicit feedback. We compare the proposed algorithms with industry standard methods based on 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 produces the best results.

Keywords: e-commerce; quality of service; consumer behaviour; music recommender systems; interactive radio network; hybrid recommender system; information fusion; CRM (search for similar items in EconPapers)
JEL-codes: C6 C7 C8 M13 M15 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2014
New Economics Papers: this item is included in nep-cis and nep-ict
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Published in WP BRP Series: Management / MAN, December 2014, pages 1-26

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