Next Chain Prediction: A Generative Recommendation Model with Sequence-Chain Attention

Sequential recommendation is a classic task in the field of recommendation, which aims to predict the next user-preferred item based on their historical interactions. However, in practical scenarios, users' needs are dynamically evolving in a short period and exhibit a chain-like structure. Consequently, recommending only the next single item does not fully meet user demands and limits the potential for increasing business traffic on platforms. To overcome this limitation, we propose a new recommendation paradigm, Next Chain Prediction, which requires the model to predict a chain of items. Due to the advantages of generative recommendation models on user preference representation and scalability, we design a generative recommendation model for next chain prediction. The generative model extracts long-term interests and short-term demands within a unified framework. By designing a Sequence-Chain Attention mechanism, the model performs self-attention learning across dual dimensions. Additionally, we design a generative loss function to balance the hit rate and diversity of the recommended items in the chain. We conduct experiments across three datasets and the experimental results show that our method achieves at least 1.22% improvement in HR@10 across three datasets for recommending multi-item chains. Furthermore, our method improves the diversity of recommended items and also offers the flexibility to adjust the size of predicted chains, maintaining state-of-the-art performance even when limited to predicting a single item.