ORCID Profile
0000-0002-0350-0313
Current Organisation
Technische Universiteit Delft
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Publisher: ACM
Date: 13-05-2019
Publisher: Elsevier BV
Date: 09-2019
Publisher: ACM
Date: 22-09-2020
Publisher: ACM
Date: 06-11-2017
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2017
Abstract: Representation learning (RL) has recently proven to be effective in capturing local item relationships by modeling item co-occurrence in in idual 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.
Publisher: ACM
Date: 07-09-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer International Publishing
Date: 2019
Publisher: ACM
Date: 27-09-2018
No related grants have been discovered for Jie Yang.