协同竞争过滤:使用用户选择上下文学习推荐

Shuang-Hong Yang, Bo Long, Alex Smola, H. Zha, Zhaohui Zheng
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引用次数: 150

摘要

虽然用户的偏好直接反映在她和推荐人之间的交互选择过程中,但这种丰富的信息并没有被充分利用来学习推荐模型。特别是,现有的协同过滤(CF)方法只考虑用户行为的二进制事件,而完全忽略了用户做出决策的上下文。在本文中,我们提出了协作竞争过滤(CCF),这是一个通过建模推荐系统中的选择过程来学习用户偏好的框架。CCF采用乘法潜因子模型来表征二元效用函数。但与CF不同的是,CCF通过编码局部竞争效应来模拟用户的选择行为。通过这种方式,CCF允许我们利用以前与现有CF模型中缺失的数据集中在一起的二元数据。我们提出了两种公式和一种高效的大规模优化算法。在三个真实世界推荐数据集上的实验表明,CCF在离线和在线评估方面都明显优于标准的CF方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Collaborative competitive filtering: learning recommender using context of user choice
While a user's preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learning recommender models. In particular, existing collaborative filtering (CF) approaches take into account only the binary events of user actions but totally disregard the contexts in which users' decisions are made. In this paper, we propose Collaborative Competitive Filtering (CCF), a framework for learning user preferences by modeling the choice process in recommender systems. CCF employs a multiplicative latent factor model to characterize the dyadic utility function. But unlike CF, CCF models the user behavior of choices by encoding a local competition effect. In this way, CCF allows us to leverage dyadic data that was previously lumped together with missing data in existing CF models. We present two formulations and an efficient large scale optimization algorithm. Experiments on three real-world recommendation data sets demonstrate that CCF significantly outperforms standard CF approaches in both offline and online evaluations.
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