Collaborative Filtering for Implicit Feedback Datasets
Top Cited Papers
- 1 December 2008
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 15504786,p. 263-272
- https://doi.org/10.1109/icdm.2008.22
Abstract
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 shows. It compares favorably with well tuned implementations of other known methods. In addition, we offer a novel way to give explanations to recommendations given by this factor model.Keywords
This publication has 14 references indexed in Scilit:
- Major components of the gravity recommendation systemACM SIGKDD Explorations Newsletter, 2007
- A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerceIEEE Intelligent Systems, 2007
- Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation WeightsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Modeling relationships at multiple scales to improve accuracy of large recommender systemsPublished by Association for Computing Machinery (ACM) ,2007
- Restricted Boltzmann machines for collaborative filteringPublished by Association for Computing Machinery (ACM) ,2007
- Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensionsIEEE Transactions on Knowledge and Data Engineering, 2005
- Latent semantic models for collaborative filteringACM Transactions on Information Systems, 2004
- Amazon.com recommendations: item-to-item collaborative filteringIEEE Internet Computing, 2003
- Item-based collaborative filtering recommendation algorithmsPublished by Association for Computing Machinery (ACM) ,2001
- Explaining collaborative filtering recommendationsPublished by Association for Computing Machinery (ACM) ,2000