  Session-based recommendation (SBR) is proposed to recommend items within
short sessions given that user profiles are invisible in various scenarios
nowadays, such as e-commerce and short video recommendation. There is a common
scenario that user specifies a target category of items as a global filter,
however previous SBR settings mainly consider the item sequence and overlook
the rich target category information. Therefore, we define a new task called
Category-aware Session-Based Recommendation (CSBR), focusing on the above
scenario, in which the user-specified category can be efficiently utilized by
the recommendation system. To address the challenges of the proposed task, we
develop a novel method called Intention Adaptive Graph Neural Network (IAGNN),
which takes advantage of relationship between items and their categories to
achieve an accurate recommendation result. Specifically, we construct a
category-aware graph with both item and category nodes to represent the complex
transition information in the session. An intention-adaptive graph neural
network on the category-aware graph is utilized to capture user intention by
transferring the historical interaction information to the user-specified
category domain. Extensive experiments on three real-world datasets are
conducted to show our IAGNN outperforms the state-of-the-art baselines in the
new task.
