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Collaborative Filtering (CF) is a widely adopted technique in recommendersystems. Traditional CF models mainly focus on predicting a user's preferenceto the items in a single domain such as the movie domain or the music domain. Amajor challenge for such models is the data sparsity problem, and especially,CF cannot make accurate predictions for the cold-start users who have noratings at all. Although Cross-Domain Collaborative Filtering (CDCF) isproposed for effectively transferring users' rating preference across differentdomains, it is still difficult for existing CDCF models to tackle thecold-start users in the target domain due to the extreme data sparsity. In thispaper, we propose a Cross-Domain Latent Feature Mapping (CDLFM) model forcold-start users in the target domain. Firstly, in order to better characterizeusers in sparse domains, we take the users' similarity relationship on ratingbehaviors into consideration and propose the Matrix Factorization byincorporating User Similarities (MFUS) in which three similarity measures areproposed. Next, to perform knowledge transfer across domains, we propose aneighborhood based gradient boosting trees method to learn the cross-domainuser latent feature mapping function. For each cold-start user, we learnhis/her feature mapping function based on the latent feature pairs of thoselinked users who have similar rating behaviors with the cold-start user in theauxiliary domain. And the preference of the cold-start user in the targetdomain can be predicted based on the mapping function and his/her latentfeatures in the auxiliary domain. Experimental results on two real data setsextracted from Amazon transaction data demonstrate the superiority of ourproposed model against other state-of-the-art methods.

Cross-Domain Recommendation for Cold-Start Users via Neighborhood Based Feature Mapping