About: A Hybrid Recommendation for Music Based on Reinforcement Learning

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A Hybrid Recommendation for Music Based on Reinforcement Learning
Creator	Wang, Yu
Source	PMC
abstract	The key to personalized recommendation system is the prediction of users’ preferences. However, almost all existing music recommendation approaches only learn listeners’ preferences based on their historical records or explicit feedback, without considering the simulation of interaction process which can capture the minor changes of listeners’ preferences sensitively. In this paper, we propose a personalized hybrid recommendation algorithm for music based on reinforcement learning (PHRR) to recommend song sequences that match listeners’ preferences better. We firstly use weighted matrix factorization (WMF) and convolutional neural network (CNN) to learn and extract the song feature vectors. In order to capture the changes of listeners’ preferences sensitively, we innovatively enhance simulating interaction process of listeners and update the model continuously based on their preferences both for songs and song transitions. The extensive experiments on real-world datasets validate the effectiveness of the proposed PHRR on song sequence recommendation compared with the state-of-the-art recommendation approaches.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1007/978-3-030-47426-3_8
has license	no-cc
sha1sum (hex)	8e1ed71319f7ec5587bf53e84bcc8f4d411c6da7
schema:url	https://doi.org/10.1007/978-3-030-47426-3_8
resource representing a document's title	A Hybrid Recommendation for Music Based on Reinforcement Learning
has PubMed Central identifier	PMC7206183
schema:publication	Advances in Knowledge Discovery and Data Mining
resource representing a document's body	covid:8e1ed71319f7ec5587bf53e84bcc8f4d411c6da7#body_text
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