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Indexed by:期刊论文

Date of Publication:2014-01-01

Journal:Journal of Industrial and Production Engineering

Included Journals:EI、Scopus

Volume:31

Issue:1

Page Number:17-26

ISSN No.: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.