{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T14:57:19Z","timestamp":1773932239862,"version":"3.50.1"},"reference-count":64,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T00:00:00Z","timestamp":1692316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2024,1,31]]},"abstract":"<jats:p>\n            Traditional music recommender systems are mainly based on users\u2019 interactions, which limit their performance. Particularly, various kinds of content information, such as metadata and description can be used to improve music recommendation. However, it remains to be addressed how to fully incorporate the rich auxiliary\/side information and effectively deal with heterogeneity in it. In this paper, we propose a\n            <jats:bold>M<\/jats:bold>\n            ulti-view\n            <jats:bold>E<\/jats:bold>\n            nhanced\n            <jats:bold>G<\/jats:bold>\n            raph\n            <jats:bold>A<\/jats:bold>\n            ttention\n            <jats:bold>N<\/jats:bold>\n            etwork (named\n            <jats:bold>MEGAN<\/jats:bold>\n            ) for session-based music recommendation. MEGAN can learn informative representations (embeddings) of music pieces and users from heterogeneous information based on graph neural network and attention mechanism. Specifically, the proposed approach MEGAN firstly models users\u2019 listening behaviors and the textual content of music pieces with a Heterogeneous Music Graph (HMG). Then, a devised Graph Attention Network is used to learn the low-dimensional embedding of music pieces and users and by integrating various kinds of information, which is enhanced by multi-view from HMG in an adaptive and unified way. Finally, users\u2019 hybrid preferences are learned from users\u2019 listening behaviors and music pieces that satisfy users real-time requirements are recommended. Comprehensive experiments are conducted on two real-world datasets, and the results show that MEGAN achieves better performance than baselines, including several state-of-the-art recommendation methods.\n          <\/jats:p>","DOI":"10.1145\/3592853","type":"journal-article","created":{"date-parts":[[2023,5,20]],"date-time":"2023-05-20T08:59:21Z","timestamp":1684573161000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":32,"title":["Multi-View Enhanced Graph Attention Network for Session-Based Music Recommendation"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2152-0446","authenticated-orcid":false,"given":"Dongjing","family":"Wang","sequence":"first","affiliation":[{"name":"Hangzhou Dianzi University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3416-839X","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hangzhou Dianzi University, China, Hangzhou Dianzi University Shangyu Institute of Science and Engineering, China, and Nanjing University of Science and Technology, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7565-4111","authenticated-orcid":false,"given":"Yuyu","family":"Yin","sequence":"additional","affiliation":[{"name":"Hangzhou Dianzi University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8919-1613","authenticated-orcid":false,"given":"Dongjin","family":"Yu","sequence":"additional","affiliation":[{"name":"Hangzhou Dianzi University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4493-6663","authenticated-orcid":false,"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5015-6095","authenticated-orcid":false,"given":"Shuiguang","family":"Deng","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}]}],"member":"320","published-online":{"date-parts":[[2023,8,18]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-13287-2_4"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-13287-2_3"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080797"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2879859"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441792"},{"key":"e_1_3_2_8_2","article-title":"Content-driven music recommendation: Evolution, state of the art, and challenges","author":"Deldjoo Yashar","year":"2021","unstructured":"Yashar Deldjoo, Markus Schedl, and Peter Knees. 2021. 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