MRLR

multi-level representation learning for personalized ranking in recommendation

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

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

8 Citations (Scopus)

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 languageEnglish
Title of host publication26th International Joint Conference on Artificial Intelligence, IJCAI 2017
EditorsCarles Sierra
PublisherInternational Joint Conferences on Artificial Intelligence
Pages2807-2813
Number of pages7
ISBN (Electronic)9780999241103
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017

Conference

Conference26th International Joint Conference on Artificial Intelligence, IJCAI 2017
CountryAustralia
CityMelbourne
Period19/08/1725/08/17

Cite this

Sun, Z., Yang, J., Zhang, J., Bozzon, A., Chen, Y., & Xu, C. (2017). MRLR: multi-level representation learning for personalized ranking in recommendation. In C. Sierra (Ed.), 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 (pp. 2807-2813). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2017/391