Towards More Personalized Recommendations by Modeling Users? Temporal Behaviors with Task-Based Graph Neural Network (TGNN)

A recommender system is tasked with effectively analyzing a user’s preferences and interactions to provide personalized recommendations. This calls for extracting and connecting various heterogeneous data while preserving their temporal relations. Graph neural networks (GNNs) have proven to be highly suitable in recommendation systems for connecting different types of user behavioral signals. However, they inherently lack ability to capture temporal aspects of underlying data. This shortcoming prevents them from explicating and utilizing task information, which is shown to be instrumental in many information retrieval applications. To overcome this limitation, we propose a new Task-based Graph Neural Network model (TGNN) focusing on identifying users’ underlying tasks within their temporal multi-behavior, specifically in each session. The model consists of three modules: (1) a sequential meta-path module that captures a temporal sequence of users’ behaviors; (2) a graph neural network layer that models the relationships between different information items and users into task representations; and (3) a recommendation layer that utilizes a collaborative filtering method to generate top-N recommendations based on the model’s comprehension of users’ tasks. The novelty of our approach lies in understanding users’ tasks through their temporal behavior, enabling more accurate personalization. The results of evaluative experiments on three publicly available datasets demonstrate the effectiveness of our task-based recommendation model compared to 10 baselines and indicate a promising research direction for task-oriented recommender systems.