Release Time:2019-03-11  Hits:

Indexed by: Journal Article

Date of Publication: 2014-01-01

Journal: Journal of Industrial and Production Engineering

Included Journals: Scopus、EI

Volume: 31

Issue: 1

Page Number: 17-26

ISSN: 21681015

Abstract: Recommender systems are widely used to help user select relevant online information. A key challenge of recommender systems is to provide high-quality recommendations for cold-start users or cold-start items. We propose a feature-based regression algorithm with baseline estimates to cope with three types of cold-start problems: cold-start system, cold-start users, and cold-start items. We consider all available information of users and items to solve the cold-start problems and take into account the user and item effects that exist in collaborative filtering systems. Compared to some existing algorithms, our algorithm is effective on the 100 k MovieLens data-set for cold-start recommendation. © 2014 Chinese Institute of Industrial Engineers.