Anchor-based Collaborative Filtering

Oren Barkan, Roy Hirsch, Ori Katz, Avi Caciularu, Noam Koenigstein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations


Modern-day recommender systems are often based on learning representations in a latent vector space that encode user and item preferences. In these models, each user/item is represented by a single vector and user-item interactions are modeled by some function over the corresponding vectors. This paradigm is common to a large body of collaborative filtering models that repeatedly demonstrated superior results. In this work, we break away from this paradigm and present ACF: Anchor-based Collaborative Filtering. Instead of learning unique vectors for each user and each item, ACF learns a spanning set of anchor-vectors that commonly serve both users and items. In ACF, each anchor corresponds to a unique "taste'' and users/items are represented as a convex combination over the spanning set of anchors. Additionally, ACF employs two novel constraints: (1) exclusiveness constraint on item-to-anchor relations that encourages each item to pick a single representative anchor, and (2) an inclusiveness constraint on anchors-to-items relations that encourages full utilization of all the anchors. We compare ACF with other state-of-the-art alternatives and demonstrate its effectiveness on multiple datasets.

Original languageEnglish
Title of host publicationCIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Number of pages5
ISBN (Electronic)9781450384469
StatePublished - 26 Oct 2021
Event30th ACM International Conference on Information and Knowledge Management, CIKM 2021 - Virtual, Online, Australia
Duration: 1 Nov 20215 Nov 2021

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings


Conference30th ACM International Conference on Information and Knowledge Management, CIKM 2021
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021 ACM.


  • clustering, machine learning
  • collaborative filtering
  • recommender systems
  • representation learning


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