计算机科学
协同过滤
约束(计算机辅助设计)
集合(抽象数据类型)
推荐系统
编码
空格(标点符号)
功能(生物学)
向量空间
人工智能
人机交互
理论计算机科学
情报检索
机器学习
数学
进化生物学
生物
基因
操作系统
生物化学
化学
程序设计语言
几何学
作者
Oren Barkan,Roy Hirsch,Ori Katz,Avi Caciularu,Noam Koenigstein
标识
DOI:10.1145/3459637.3482056
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI