Pairwise preference regression for cold-start recommendation
- 23 October 2009
- conference paper
- conference paper
- Published by Association for Computing Machinery (ACM)
Abstract
Recommender systems are widely used in online e-commerce applications to improve user engagement and then to increase revenue. A key challenge for recommender systems is providing high quality recommendation to users in ``cold-start" situations. We consider three types of cold-start problems: 1) recommendation on existing items for new users; 2) recommendation on new items for existing users; 3) recommendation on new items for new users. We propose predictive feature-based regression models that leverage all available information of users and items, such as user demographic information and item content features, to tackle cold-start problems. The resulting algorithms scale efficiently as a linear function of the number of observations. We verify the usefulness of our approach in three cold-start settings on the MovieLens and EachMovie datasets, by comparing with five alternatives including random, most popular, segmented most popular, and two variations of Vibes affinity algorithm widely used at Yahoo! for recommendation.Keywords
This publication has 24 references indexed in Scilit:
- A tensor higher-order singular value decomposition for integrative analysis of DNA microarray data from different studiesProceedings of the National Academy of Sciences of the United States of America, 2007
- Naïve filterbots for robust cold-start recommendationsPublished by Association for Computing Machinery (ACM) ,2006
- Collaborative prediction using ensembles of Maximum Margin Matrix FactorizationsPublished by Association for Computing Machinery (ACM) ,2006
- Taxonomy-driven computation of product recommendationsPublished by Association for Computing Machinery (ACM) ,2004
- Methods and metrics for cold-start recommendationsPublished by Association for Computing Machinery (ACM) ,2002
- Item-based collaborative filtering recommendation algorithmsPublished by Association for Computing Machinery (ACM) ,2001
- Horting hatches an eggPublished by Association for Computing Machinery (ACM) ,1999
- Support vector learning for ordinal regressionPublished by Institution of Engineering and Technology (IET) ,1999
- FabCommunications of the ACM, 1997
- Some mathematical notes on three-mode factor analysisPsychometrika, 1966