  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.
