NDLI: Pairwise preference regression for cold-start recommendation

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Pairwise preference regression for cold-start recommendation

Content Provider	ACM Digital Library
Author	Chu, Wei Park, Seung-Taek
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.
Starting Page	21
Ending Page	28
Page Count	8
File Format	PDF
ISBN	9781605584355
DOI	10.1145/1639714.1639720
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-10-23
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Ranking Cold-start problems Recommender system User and item features Normalized discounted cumulative gain
Content Type	Text
Resource Type	Article

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