Shixuan Zhu

Chuan Cui, Qi Shen, Shixuan Zhu et al.

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.

Qi Shen, Shixuan Zhu, Yitong Pang et al.

Session-based recommendation (SBR) is a challenging task, which aims at recommending next items based on anonymous interaction sequences. Despite the superior performance of existing methods for SBR, there are still several limitations: (i) Almost all existing works concentrate on single interest extraction and fail to disentangle multiple interests of user, which easily results in suboptimal representations for SBR. (ii) Furthermore, previous methods also ignore the multi-form temporal information, which is significant signal to obtain current intention for SBR. To address the limitations mentioned above, we propose a novel method, called \emph{Temporal aware Multi-Interest Graph Neural Network} (TMI-GNN) to disentangle multi-interest and yield refined intention representations with the injection of two level temporal information. Specifically, by appending multiple interest nodes, we construct a multi-interest graph for current session, and adopt the GNNs to model the item-item relation to capture adjacent item transitions, item-interest relation to disentangle the multi-interests, and interest-item relation to refine the item representation. Meanwhile, we incorporate item-level time interval signals to guide the item information propagation, and interest-level time distribution information to assist the scattering of interest information. Experiments on three benchmark datasets demonstrate that TMI-GNN outperforms other state-of-the-art methods consistently.