Cross-domain recommendation can study the knowledge of the auxiliary domains to enrich the knowledge in the target domain

so as to improve the recommendation precision in the target domain.This paper proposes an aggregation and transfer collaborative filtering algorithm for cross-domain recommendation (ATCF).In order to represent the sharing knowledge in different domains

the knowledge in the auxiliary domain and the target domain are fully aggregated

through two levels of matrix concatenation.Moreover

the personalized knowledge of the target domain is represented by knowledge transferring from auxiliary domain.By fusion the sharing and the personalized knowledge

we can obtain the final rating.Two different cross-domain datasets are used for the experiments.Our efforts show that the ATCF algorithm has better recommendation performance.