MRLR: Multi-level Representation Learning for Personalized Ranking in Recommendation

MRLR: Multi-level Representation Learning for Personalized Ranking in Recommendation

Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Yu Chen, Chi Xu

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 2807-2813. https://doi.org/10.24963/ijcai.2017/391

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.
Keywords:
Machine Learning: Learning Preferences or Rankings
Multidisciplinary Topics and Applications: Personalization and User Modeling