A context-aware recommendation system based on latent factor model

Abstract

Recommendation systems are the effective tools that get over the information overload problem, providing users with the most appropriate things by considering their personal preferences. The interaction contextual information is considered to help improve the accuracy of the recommendation results. A few previous studies have tried to put to use contextual information to the recommendation system. In this paper, we propose a new recommendation model called C-LFM, which adds contextual information to Latent Factor Model (LFM) to improve recommendation results. Different from the existing recommendation method, C-LFM regards contextual information as a factor of LFM. Extensive experiments are conducted, and the experimental results present the effectiveness of C-LFM to us.