<p>Cold start is a challenge in the field of recommender systems. Cross-domain Recommendation (CDR), as a promising approach to solve this problem, focuses on how to effectively transfer user preferences from one domain to another. Current approaches have the following issues: user’s interests extraction depending on priori knowledge; noise being easily introduced and negative transfer arising when user’s features are transferred; users’ overall features and diverse interests being less considered together. This paper proposes a novel cross-domain recommendation method named Cross-domain Recommendation based on Three-Phase Cross-Attention and Multi-Granularity Transfer Meta-Network (CAMGCDR). The self-attention mechanism is adopted to extract the interest features from the user’s behavioral sequences, so that there is no more relying on priori knowledge. The item-level cross-attention is used to fine-tune the embedding vectors of the user’s historical behavior in the source domain. The part of the user’s historical behaviors in the source domain that are most relevant to the item in target domain are paid attention, resulting in less noise and negative transfer. By constructing mapping bridges with two granularities, the user embedding vectors and multi-interest vectors are mapped to target domain respectively. User embedding vector is concatenated with multi-interest vectors so that both overall features of the user and the diverse interests are taken into account. Thus, the accuracy and personalization recommendation of CAMGCDR are improved. Experimental results on large-scale real-world datasets show that the CAMGCDR outperforms all the baseline methods, proving its effectiveness and practicality.</p>