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A hybrid recommender system for improving automatic playlist continuation

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Postprint definitiu - Juliol 2019 (1,360Mb)
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10.1109/TKDE.2019.2952099
 
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hdl:2117/188974

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Gatzioura, Anna
Vinagre, João
Jorge, Alípio Mário
Sànchez-Marrè, MiquelMés informacióMés informacióMés informació
Document typeArticle
Defense date2021-05-01
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
Although widely used, the majority of current music recommender systems still focus on recommendations’ accuracy, userpreferences and isolated item characteristics, without evaluating other important factors, like the joint item selections and the recommendation moment. However, when it comes to playlist recommendations, additional dimensions, as well as the notion of user experience and perception, should be taken into account to improve recommendations’ quality. In this work, HybA, a hybrid recommender system for automatic playlist continuation, that combines Latent Dirichlet Allocation and Case-Based Reasoning, is proposed. This system aims to address “similar concepts” rather than similar users. More than generating a playlist based on user requirements, like automatic playlist generation methods, HybA identifies the semantic characteristics of a started playlist and reuses the most similar past ones, to recommend relevant playlist continuations. In addition, support to beyond accuracy dimensions, like increased coherence or diverse items’ discovery, is provided. To overcome the semantic gap between music descriptions and user preferences, identify playlist structures and capture songs’ similarity, a graph model is used. Experiments on real datasets have shown that the proposed algorithm is able to outperform other state of the art techniques, in terms of accuracy, while balancing between diversity and coherence.
CitationGatzioura, A. [et al.]. A hybrid recommender system for improving automatic playlist continuation. "IEEE transactions on knowledge and data engineering", 1 Maig 2021, vol. 33, núm. 5, p. 1819-1830. 
URIhttp://hdl.handle.net/2117/188974
DOI10.1109/TKDE.2019.2952099
ISSN1041-4347
Publisher versionhttps://ieeexplore.ieee.org/document/8894369
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  • Departament de Ciències de la Computació - Articles de revista [909]
  • KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic - Articles de revista [109]
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