MRLR: Multi-Level Representation Learning for Personalized Ranking in Recommendation
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.
Cite
Text
Sun et al. "MRLR: Multi-Level Representation Learning for Personalized Ranking in Recommendation." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/391Markdown
[Sun et al. "MRLR: Multi-Level Representation Learning for Personalized Ranking in Recommendation." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/sun2017ijcai-mrlr/) doi:10.24963/IJCAI.2017/391BibTeX
@inproceedings{sun2017ijcai-mrlr,
title = {{MRLR: Multi-Level Representation Learning for Personalized Ranking in Recommendation}},
author = {Sun, Zhu and Yang, Jie and Zhang, Jie and Bozzon, Alessandro and Chen, Yu and Xu, Chi},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {2807-2813},
doi = {10.24963/IJCAI.2017/391},
url = {https://mlanthology.org/ijcai/2017/sun2017ijcai-mrlr/}
}