Abstract: Traditional single-domain recommendation algorithm is limited by the sparse relationship between users and items,and there is a problem of user/item cold start,and only models the item ratings by users,ignoring the information contained in the review text.The cross-domain recommendation algorithm based on review text extracts user/item review information in the auxiliary domain to alleviate data sparseness in the target domain and improve the accuracy of recommendation.This paper proposes a cross-domain recommendation algorithm SAMTL that combines self-attention mechanism and transfer learning.Different from existing algorithms,SAMTL fully integrates the knowledge of the target domain and auxiliary domains.Firstly,the self-attention mechanism is introduced to model user’s preference information.Then,by the cross-mapping cross-domain transmission network,the recommendation accuracy of another domain is improved with the help of information in one domain.Finally,the information of the two domains is integrated in the knowledge fusion and scoring prediction module to perform scoring prediction.Experiments on Amazon data set show that,compared with the existing cross-domain recommendation model,SAMTL has higher MAE and MSE values,and MAE increases by 8.4%,13.2% and 19.4% on three different cross-domain data sets,MSE increases by 6.3%,7.8% and 5.6% respectively.A number of experiments verify the effectiveness of self-attention mechanism and transfer learning,as well as the advantages in alleviating data sparsity and user cold start problems.

Key words: Comment text, Cross-domain, Recommendation system, Self-attention, Transfer learning