Abstract
Traditional music recommender systems are mainly based on users’ 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 Multi-view Enhanced Graph Attention Network (named MEGAN) 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’ 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’ hybrid preferences are learned from users’ 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.
- [1] . 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17, 6 (2005), 734–749. Google ScholarDigital Library
- [2] . 2010. The long tail in recommender systems. In Music Recommendation and Discovery. Springer, Berlin, Heidelberg, 87–107. Google ScholarCross Ref
- [3] . 2010. Music recommendation. In Music Recommendation and Discovery. Springer, Berlin, Heidelberg, 43–85. Google ScholarCross Ref
- [4] . 2017. Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (Shinjuku, Tokyo, Japan) (
SIGIR ’17 ). ACM, Association for Computing Machinery, New York, NY, USA, 335–344. Google ScholarDigital Library - [5] . 2020. Embedding attention and residual network for accurate salient object detection. IEEE Transactions on Cybernetics 50, 5 (2020), 2050–2062. Google ScholarCross Ref
- [6] . 2021. An efficient and effective framework for session-based social recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 400–408.Google ScholarDigital Library
- [7] . 2021. Content-driven music recommendation: Evolution, state of the art, and challenges. arXiv preprint arXiv:2107.11803 (2021).Google Scholar
- [8] . 2015. Exploring user emotion in microblogs for music recommendation. Expert Systems with Applications 42, 23 (2015), 9284–9293. Google ScholarDigital Library
- [9] . 2004. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS) 22, 1 (2004), 143–177. Google ScholarDigital Library
- [10] . 2020. Improving implicit recommender systems with auxiliary data. ACM Transactions on Information Systems (TOIS) 38, 1 (2020), 1–27. Google ScholarDigital Library
- [11] . 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (
KDD ’16 ). Association for Computing Machinery, New York, NY, USA, 855–864. Google ScholarDigital Library - [12] . 2021. Hierarchical hyperedge embedding-based representation learning for group recommendation. ACM Transactions on Information Systems (TOIS) 40, 1 (2021), 1–27. Google ScholarDigital Library
- [13] . 2019. Streaming session-based recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1569–1577.Google ScholarDigital Library
- [14] . 2020. Adaptive deep modeling of users and items using side information for recommendation. IEEE Transactions on Neural Networks and Learning Systems 31, 3 (2020), 737–748. Google ScholarCross Ref
- [15] . 2021. A multi-attention collaborative deep learning approach for blood pressure prediction. ACM Transactions on Management Information Systems (TMIS) 13, 2 (2021), 1–20. Google ScholarDigital Library
- [16] . 2021. An attention-based spatiotemporal lstm network for next poi recommendation. IEEE Transactions on Services Computing 14, 6 (2021), 1585–1597. Google ScholarCross Ref
- [17] . 2020. An efficient group recommendation model with multiattention-based neural networks. IEEE Transactions on Neural Networks and Learning Systems 31, 11 (2020), 4461–4474. Google ScholarCross Ref
- [18] . 2013. Location-aware music recommendation using auto-tagging and hybrid matching. In Proceedings of the 7th ACM Conference on Recommender Systems (Hong Kong, China) (
RecSys ’13 ). ACM, Association for Computing Machinery, New York, NY, USA, 17–24. Google ScholarDigital Library - [19] . 2018. Self-attentive sequential recommendation. In 2018 IEEE International Conference on Data Mining (ICDM) (Singapore). IEEE, IEEE, 197–206. Google ScholarCross Ref
- [20] . 2015. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings.Google Scholar
- [21] . 2022. Music recommendation via hypergraph embedding. IEEE Transactions on Neural Networks and Learning Systems (2022).Google Scholar
- [22] . 2017. A smartphone-based activity-aware system for music streaming recommendation. Knowledge-Based Systems 131 (2017), 70–82. Google ScholarCross Ref
- [23] . 2019. Personalised reranking of paper recommendations using paper content and user behavior. ACM Transactions on Information Systems (TOIS) 37, 3 (2019), 1–23. Google ScholarDigital Library
- [24] . 2015. Gated graph sequence neural networks. In 4th International Conference on Learning Representations, ICLR 2016.Google Scholar
- [25] . 2003. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7, 1 (2003), 76–80. Google ScholarDigital Library
- [26] . 2019. Hierarchical gating networks for sequential recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Anchorage, AK, USA) (
KDD ’19 ). Association for Computing Machinery, New York, NY, USA, 825–833. Google ScholarDigital Library - [27] . 2017. Sound and music recommendation with knowledge graphs. ACM Transactions on Intelligent Systems and Technology (TIST) 8, 2 (2017), 21. Google ScholarDigital Library
- [28] . 2017. Interacting attention-gated recurrent networks for recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (Singapore, Singapore) (
CIKM ’17 ). Association for Computing Machinery, New York, NY, USA, 1459–1468. Google ScholarDigital Library - [29] . 2020. Gag: Global attributed graph neural network for streaming session-based recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 669–678.Google ScholarDigital Library
- [30] . 2010. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web (Raleigh, North Carolina, USA) (
WWW ’10 ). ACM, Association for Computing Machinery, New York, NY, USA, 811–820. Google ScholarDigital Library - [31] . 2016. A collaborative filtering method for music recommendation using playing coefficients for artists and users. Expert Systems with Applications 66 (2016), 234–244. Google ScholarDigital Library
- [32] . 2017. Hierarchical contextual attention recurrent neural network for map query suggestion. IEEE Transactions on Knowledge and Data Engineering 29, 9 (2017), 1888–1901. Google ScholarDigital Library
- [33] . 2019. Session-based social recommendation via dynamic graph attention networks. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (Melbourne VIC, Australia) (
WSDM ’19 ). Association for Computing Machinery, New York, NY, USA, 555–563. Google ScholarDigital Library - [34] . 2015. Line: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web (Florence, Italy) (
WWW ’15 ). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1067–1077. Google ScholarDigital Library - [35] . 2015. 30Music listening and playlists dataset. In Poster Proceedings of the 9th ACM Conference on Recommender Systems.Google Scholar
- [36] . 2017. Attention is all you need. In Advances in Neural Information Processing Systems (Long Beach, California, USA) (
NIPS’17 , Vol. 30). Curran Associates Inc., Red Hook, NY, USA, 6000–6010.Google Scholar - [37] . 2018. Sequence-based context-aware music recommendation. Information Retrieval Journal 21, 2-3 (2018), 230–252. Google ScholarDigital Library
- [38] . 2018. Learning to embed music and metadata for context-aware music recommendation. World Wide Web 21, 5 (2018), 1399–1423. Google ScholarDigital Library
- [39] . 2021. Attentive sequential model based on graph neural network for next poi recommendation. World Wide Web 24, 6 (2021), 2161–2184. Google ScholarDigital Library
- [40] . 2021. Sequential recommendation based on multivariate hawkes process embedding with attention. IEEE Transactions on Cybernetics (2021), 1–13. Google ScholarCross Ref
- [41] . 2020. Came: Content-and context-aware music embedding for recommendation. IEEE Transactions on Neural Networks and Learning Systems 32, 3 (2020), 1375–1388. Google ScholarCross Ref
- [42] . 2015. Learning hierarchical representation model for NextBasket recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (Santiago, Chile) (
SIGIR ’15 ). ACM, Association for Computing Machinery, New York, NY, USA, 403–412. Google ScholarDigital Library - [43] . 2019. Kgat: Knowledge graph attention network for recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Anchorage, AK, USA) (
KDD ’19 ). Association for Computing Machinery, New York, NY, USA, 950–958. Google ScholarDigital Library - [44] . 2019. Neural graph collaborative filtering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Paris, France) (
SIGIR’19 ). Association for Computing Machinery, New York, NY, USA, 165–174. Google ScholarDigital Library - [45] . 2021. Learning intents behind interactions with knowledge graph for recommendation. In Proceedings of the Web Conference 2021. 878–887.Google ScholarDigital Library
- [46] . 2019. Heterogeneous graph attention network. In The World Wide Web Conference (San Francisco, CA, USA) (
WWW ’19 ). Association for Computing Machinery, New York, NY, USA, 2022–2032. Google ScholarDigital Library - [47] . 2014. Improving content-based and hybrid music recommendation using deep learning. In Proceedings of the 22nd ACM International Conference on Multimedia (Orlando, Florida, USA) (
MM ’14 ). Association for Computing Machinery, New York, NY, USA, 627–636. Google ScholarDigital Library - [48] . 2020. Global context enhanced graph neural networks for session-based recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 169–178.Google ScholarDigital Library
- [49] . 2020. Diffnet++: A neural influence and interest diffusion network for social recommendation. IEEE Transactions on Knowledge and Data Engineering (2020).Google Scholar
- [50] . 2019. Session-based recommendation with graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 346–353. Google ScholarDigital Library
- [51] . 2010. Temporal recommendation on graphs via long-and short-term preference fusion. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Washington, DC, USA) (
KDD ’10 ). ACM, Association for Computing Machinery, New York, NY, USA, 723–732. Google ScholarDigital Library - [52] . 2017. Attentional factorization machines: Learning the weight of feature interactions via attention networks. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (Melbourne, Australia) (
IJCAI’17 ). AAAI Press, 3119–3125.Google ScholarCross Ref - [53] . 2019. Graph contextualized self-attention network for session-based recommendation.. In IJCAI, Vol. 19. 3940–3946.Google Scholar
- [54] . 2019. Music playlist recommendation with long short-term memory. In International Conference on Database Systems for Advanced Applications. Springer, Springer International Publishing, Cham, 416–432. Google ScholarDigital Library
- [55] . 2021. LegalGNN: Legal information enhanced graph neural network for recommendation. ACM Transactions on Information Systems (TOIS) 40, 2 (2021), 1–29. Google ScholarDigital Library
- [56] . 2021. HGAT: Heterogeneous graph attention networks for semi-supervised short text classification. ACM Transactions on Information Systems (TOIS) 39, 3 (2021), 1–29. Google ScholarDigital Library
- [57] . 2018. Sequential recommender system based on hierarchical attention network. In IJCAI International Joint Conference on Artificial Intelligence (Stockholm, Sweden) (
IJCAI’18 ). AAAI Press, 3926–3932.Google ScholarCross Ref - [58] . 2018. Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (London, United Kingdom) (
KDD ’18 ). Association for Computing Machinery, New York, NY, USA, 974–983. Google ScholarDigital Library - [59] . 2006. Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences.. In ISMIR, Vol. 6. 296–301.Google Scholar
- [60] . 2019. Heterogeneous graph neural network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Anchorage, AK, USA) (
KDD ’19 ). Association for Computing Machinery, New York, NY, USA, 793–803. Google ScholarDigital Library - [61] . 2022. Dynamic graph neural networks for sequential recommendation. IEEE Transactions on Knowledge and Data Engineering (2022). Google ScholarDigital Library
- [62] . 2021. Knowledge-enhanced session-based recommendation with temporal transformer. arXiv preprint arXiv:2112.08745 (2021).Google Scholar
- [63] . 2022. Time-aware path reasoning on knowledge graph for recommendation. ACM Transactions on Information Systems (TOIS) (2022). Google ScholarDigital Library
- [64] . 2018. MusicCNNs: A new benchmark on content-based music recommendation. In International Conference on Neural Information Processing. Springer, Springer International Publishing, Cham, 394–405. Google ScholarDigital Library
Index Terms
- Multi-View Enhanced Graph Attention Network for Session-Based Music Recommendation
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