A global contextual enhanced structural-aware transformer for sequential recommendation

Sequential recommendation (SR) has become a research hotspot recently. In our research, we observe that most existing SR models only leverage each user’s own interaction sequence to make recommendation. We argue that leveraging global contextual information across different interaction sequences could enrich item representations and thereby improve recommendation performance. To achieve this, we formulate a global graph from different sequences, providing global contextual information for each sequence. Specifically, we propose to conduct graph contrastive learning on a subgraph sampled from the global graph and a local sequence graph built from each sequence to augment item representations within each sequence. At the same time, we observe that structural dependencies, referring to relationships between items based on the graphic structure, can be extracted from the constructed global graph. Capturing structural dependencies between items may enrich the item representations. To leverage structural dependencies, we propose a new attention mechanism referred to as the Jaccard attention. While prevalent Transformer-based SR models capture semantic dependencies, referring to relationships between items based on item embeddings, in a sequence through self-attention. Therefore, it is beneficial to capture both semantic and structural dependencies between items in a sequence to further enrich item representations. Specifically, we employ two sequence encoders based on the self-attention and the proposed Jaccard attention to capture semantic and structural dependencies between items in a sequence, respectively. Overall, we propose a Global Contextual enhanced Structural-aware Transformer (GC-ST) for SR. Extensive experiments carried out on three widely used datasets demonstrate the effectiveness of GC-ST.