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/Conference proceedings/Edited volumeConference contributionScientificpeer-review

8 Citations (Scopus)
204 Downloads (Pure)

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
EditorsC. Sierra
PublisherInternational Joint Conferences on Artificial Intelligence
Pages2807-2813
Number of pages7
ISBN (Electronic)9780999241103
DOIs
Publication statusPublished - 2017
EventIJCAI 2017: 26th International Joint Conference on Artificial Intelligence - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017
Conference number: 26
http://ijcai-17.org/

Conference

ConferenceIJCAI 2017
Abbreviated titleIJCAI 2017
CountryAustralia
CityMelbourne
Period19/08/1725/08/17
Internet address

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