Collaborative Filtering for Implicit Feedback Datasets

A common task of recommender systems is to improve customer experience through personalized recommendations based on prior implicit feedback. These systems passively track different sorts of user behavior, such as purchase history, watching habits and browsing activity, in order to model user preferences. Unlike the much more extensively researched explicit feedback, we do not have any direct input from the users regarding their preferences. In particular, we lack substantial evidence on which products consumer dislike. In this work we identify unique properties of implicit feedback datasets. We propose treating the data as indication of positive and negative preference associated with vastly varying confidence levels. This leads to a factor model which is especially tailored for implicit feedback recommenders. We also suggest a scalable optimization procedure, which scales linearly with the data size. The algorithm is used successfully within a recommender system for television show...

• Type: Conference
• Year: 2008
• Where: ICDM
• Authors: Yifan Hu, Yehuda Koren, Chris Volinsky
• Keywords: Data Mining, ICDM 2008, Implicit Feedback, Prior Implicit Feedback, Recommender System