Anchor-based Collaborative Filtering

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

פרסום מחקרי: פרק בספר / בדוח / בכנספרסום בספר כנסביקורת עמיתים


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.

שפה מקוריתאנגלית
כותר פרסום המארחCIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management
מוציא לאורAssociation for Computing Machinery
מספר עמודים5
מסת"ב (אלקטרוני)9781450384469
מזהי עצם דיגיטלי (DOIs)
סטטוס פרסוםפורסם - 26 אוק׳ 2021
אירוע30th ACM International Conference on Information and Knowledge Management, CIKM 2021 - Virtual, Online, אוסטרליה
משך הזמן: 1 נוב׳ 20215 נוב׳ 2021

סדרות פרסומים

שםInternational Conference on Information and Knowledge Management, Proceedings


כנס30th ACM International Conference on Information and Knowledge Management, CIKM 2021
עירVirtual, Online

הערה ביבליוגרפית

Publisher Copyright:
© 2021 ACM.

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