Abstract
Users leave digital footprints when interacting with various music streaming services. Music play sequence, which contains rich information about personal music preference and song similarity, has been largely ignored in previous music recom-mender systems. In this paper, we explore the effects of music play sequence on developing effective personalized music recommender systems. Towards the goal, we propose to use word embedding techniques in music play sequences to estimate the similarity between songs. The learned similarity is then embedded into matrix factorization to boost the latent feature learning and discovery. Furthermore, the proposed method only considers the k-nearest songs (e.g., k = 5) in the learning process and thus avoids the increase of time complexity. Experimental results on two public datasets demonstrate that our methods could significantly improve the performance on both rating prediction and top-n recommendation tasks.
Original language | English |
---|---|
Title of host publication | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 |
Place of Publication | Melbourne; Australia |
Publisher | International Joint Conferences on Artificial Intelligence |
Pages | 3654-3660 |
Number of pages | 7 |
ISBN (Electronic) | 9780999241103 |
DOIs | |
Publication status | Published - 1 Jan 2017 |
Externally published | Yes |
Event | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia Duration: 19 Aug 2017 → 25 Aug 2017 |
Conference
Conference | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 |
---|---|
Country/Territory | Australia |
City | Melbourne |
Period | 19/08/17 → 25/08/17 |