TPUF: Enhancing Cross-domain Sequential Recommendation via Transferring Pre-trained User Features

Sequential recommendation has long been challenged by data sparsity issues. Most recently, cross-domain sequential recommendation (CDSR) techniques have been proposed to leverage sequential interaction data from other domains. However, accessing raw data from source domains is often restricted due to privacy concerns. To tackle this issue, we introduce TPUF, a novel CDSR model that transfers pre-trained latent user features from the source domain (UFS) instead of the original interaction data. By doing so, TPUF improves recommendation effectiveness while maintaining practicality. TPUF has three functional characteristics: (1) It is a feature mapping-and-aggregation framework that does not impose specific constraints on the nature of pre-trained UFS. (2) It incorporates a temporal feature mapping unit to effectively extract domain-shared information from UFS with temporal information recovered. (3) It additionally employs an adversarial feature alignment unit to align features across domains to combat feature transfer bias. Experimental results on real-world datasets demonstrate that TPUF outperforms other state-of-the-art cross-domain recommendation models and is compatible with multiple UFS types.