A feature-based regression algorithm for cold-start recommendation
Xiujuan Xu, Zhu Lizhong, Xiaowei Zhao, Zhenzhen Xu, Yu Liu · Journal of Industrial and Production Engineering · 2014
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.