  Session-based recommendation (SBR) has drawn increasingly research attention
in recent years, due to its great practical value by only exploiting the
limited user behavior history in the current session. Existing methods
typically learn the session embedding at the item level, namely, aggregating
the embeddings of items with or without the attention weights assigned to
items. However, they ignore the fact that a user's intent on adopting an item
is driven by certain factors of the item (e.g., the leading actors of an
movie). In other words, they have not explored finer-granularity interests of
users at the factor level to generate the session embedding, leading to
sub-optimal performance. To address the problem, we propose a novel method
called Disentangled Graph Neural Network (Disen-GNN) to capture the session
purpose with the consideration of factor-level attention on each item.
Specifically, we first employ the disentangled learning technique to cast item
embeddings into the embedding of multiple factors, and then use the gated graph
neural network (GGNN) to learn the embedding factor-wisely based on the item
adjacent similarity matrix computed for each factor. Moreover, the distance
correlation is adopted to enhance the independence between each pair of
factors. After representing each item with independent factors, an attention
mechanism is designed to learn user intent to different factors of each item in
the session. The session embedding is then generated by aggregating the item
embeddings with attention weights of each item's factors. To this end, our
model takes user intents at the factor level into account to infer the user
purpose in a session. Extensive experiments on three benchmark datasets
demonstrate the superiority of our method over existing methods.
