Paper Info
Title
Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering
Abstract
ABSTRACTOver the past decades, for One-Class Collaborative Filtering (OCCF), many learning objectives have been researched based on a variety of underlying probabilistic models. From our analysis, we observe that models trained with different OCCF objectives capture distinct aspects of user-item relationships, which in turn produces complementary recommendations. This paper proposes a novel OCCF framework, named as ConCF, that exploits the complementarity from heterogeneous objectives throughout the training process, generating a more generalizable model. ConCF constructs a multi-branch variant of a given target model by adding auxiliary heads, each of which is trained with heterogeneous objectives. Then, it generates consensus by consolidating the various views from the heads, and guides the heads based on the consensus. The heads are collaboratively evolved based on their complementarity throughout the training, which again results in generating more accurate consensus iteratively. After training, we convert the multi-branch architecture back to the original target model by removing the auxiliary heads, thus there is no extra inference cost for the deployment. Our extensive experiments on real-world datasets demonstrate that ConCF significantly improves the generalization of the model by exploiting the complementarity from heterogeneous objectives.
Year
DOI
Venue
2022
10.1145/3485447.3512070
International World Wide Web Conference
Keywords
DocType
Citations 
One-class collaborative filtering, Consensus learning, Learning objective, Model optimization, Recommender system
Conference
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
SeongKu Kang1214.55
Dongha Lee2146.77
Wonbin Kweon342.48
Junyoung Hwang4163.42
Hwanjo Yu51715114.02