Abstract
Representation learning (RL) has recently proven to be effective in capturing local item relationships by modeling item co-occurrence in individual user's interaction record. However, the value of RL for recommendation has not reached the full potential due to two major drawbacks: 1) recommendation is modeled as a rating prediction problem but should essentially be a personalized ranking one; 2) multi-level organizations of items are neglected for fine-grained item relationships. We design a unified Bayesian framework MRLR to learn user and item embeddings from a multi-level item organization, thus benefiting from RL as well as achieving the goal of personalized ranking. Extensive validation on real-world datasets shows that MRLR consistently outperforms state-of-the-art algorithms.
Original language | English |
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Title of host publication | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 |
Editors | C. Sierra |
Publisher | International Joint Conferences on Artificial Intelligence (IJCAI) |
Pages | 2807-2813 |
Number of pages | 7 |
ISBN (Electronic) | 9780999241103 |
DOIs | |
Publication status | Published - 2017 |
Event | IJCAI 2017: 26th International Joint Conference on Artificial Intelligence - Melbourne, Australia Duration: 19 Aug 2017 → 25 Aug 2017 Conference number: 26 http://ijcai-17.org/ |
Conference
Conference | IJCAI 2017 |
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Abbreviated title | IJCAI 2017 |
Country/Territory | Australia |
City | Melbourne |
Period | 19/08/17 → 25/08/17 |
Internet address |