CDLFM: cross-domain recommendation for cold-start users via latent feature mapping

Abstract

Collaborative filtering (CF) is a widely adopted technique in recommender systems. Traditional CF models mainly focus on predicting the user preference to items in a single domain, such as the movie domain or the music domain. A major challenge for such models is the data sparsity, and especially, CF cannot make accurate predictions for the cold-start users who have no ratings at all. Although cross-domain collaborative filtering (CDCF) is proposed for effectively transferring knowledge across different domains, it is still difficult for existing CDCF models to tackle the cold-start users in the target domain due to the extreme data sparsity. In this paper, we propose the cross-domain latent feature mapping (CDLFM) model for the cold-start users in the target domain. Firstly, in order to alleviate the data sparsity in single domain and provide essential knowledge for next step, we take users’ rating behaviors into consideration and propose the matrix factorization by incorporating user similarities. Next, to transfer knowledge across domains, we propose the neighborhood-based cross-domain latent feature mapping method. For each cold-start user, we learn his/her feature mapping function based on his/her neighbor linked users. By adopting gradient boosting trees and multilayer perceptron to model the cross-domain feature mapping function, two CDLFM models named CDLFM-GBT and CDLFM-MLP are proposed. Experimental results on two real datasets demonstrate the superiority of our proposed model against other state-of-the-art methods.