Sequential recommendation aims to predict users’ next preferred items according to their interaction sequences. Existing methods mainly utilize user-item interaction information, which may suffer from the issue of semantic information loss. In the paper, a Meta-Path guided Pre-training method for sequential Recommendation (MPPRec) is proposed to capture rich and meaningful semantic information between users and items. Specifically, MPPRec firstly learns the node embeddings guided by meta-paths in the pre-training phase. Then, the node embeddings are optimized according to task in the fine-tuning phase. Extensive experiments conducted on four real datasets demonstrate MPPRec outperforms the baseline methods.
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