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

18 Citations (Scopus)
366 Downloads (Pure)


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 (IJCAI)
Number of pages7
ISBN (Electronic)9780999241103
Publication statusPublished - 2017
EventIJCAI 2017: 26th International Joint Conference on Artificial Intelligence - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017
Conference number: 26


ConferenceIJCAI 2017
Abbreviated titleIJCAI 2017
Internet address


Dive into the research topics of 'MRLR: Multi-level representation learning for personalized ranking in recommendation'. Together they form a unique fingerprint.

Cite this