Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering

Seongku Kang, Dongha Lee, Wonbin Kweon, Junyoung Hwang, Hwanjo Yu

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

4 Citations (Scopus)


Over 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.

Original languageEnglish
Title of host publicationWWW 2022 - Proceedings of the ACM Web Conference 2022
PublisherAssociation for Computing Machinery, Inc
Number of pages12
ISBN (Electronic)9781450390965
Publication statusPublished - 2022 Apr 25
Event31st ACM World Wide Web Conference, WWW 2022 - Virtual, Online, France
Duration: 2022 Apr 252022 Apr 29

Publication series

NameWWW 2022 - Proceedings of the ACM Web Conference 2022


Conference31st ACM World Wide Web Conference, WWW 2022
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2022 ACM.

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software


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