A Hybrid Recommender System for Improving Automatic Playlist Continuation

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Gatzioura, A (Author) Other; Vinagre, J (Author) Other; Jorge, AM (Author) FCUP; Sanchez Marre, M (Author) Other

Journal

Title: IEEE Transactions on Knowledge and Data Engineering

Vol. 33

Pages: 1819-1830

ISSN: 1041-4347

Publisher: IEEE

Indexing

ISI Web of Knowledge - 8 Citations

Other information

Authenticus ID: P-00R-FP5

DOI: 10.1109/tkde.2019.2952099

Abstract (EN): Although widely used, the majority of current music recommender systems still focus on recommendations' accuracy, user preferences 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12