DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation
This addresses session-based recommendation for online video and e-commerce platforms, representing an incremental improvement over existing methods.
The paper tackles the problem of session-based recommendation by modeling users' underlying semantic demands from sequential behaviors, proposing DAGNN which extracts session demands and learns demand-aware item embeddings. The model achieves state-of-the-art performance on multiple real-world datasets.
Session-based recommendations have been widely adopted for various online video and E-commerce Websites. Most existing approaches are intuitively proposed to discover underlying interests or preferences out of the anonymous session data. This apparently ignores the fact these sequential behaviors usually reflect session user's potential demand, i.e., a semantic level factor, and therefore how to estimate underlying demands from a session is challenging. To address aforementioned issue, this paper proposes a demand-aware graph neural networks (DAGNN). Particularly, a demand modeling component is designed to first extract session demand and the underlying multiple demands of each session is estimated using the global demand matrix. Then, the demand-aware graph neural network is designed to extract session demand graph to learn the demand-aware item embedddings for the later recommendations. The mutual information loss is further designed to enhance the quality of the learnt embeddings. Extensive experiments are evaluated on several real-world datasets and the proposed model achieves the SOTA model performance.