Graph-Augmented Social Translation Model for Next-Item Recommendation
Bin Wu, Lihong Zhong, Yangdong Ye
Abstract
Next-item recommendation has been a hot research topic in academia and industry, which aims to help users discover the next interesting item. In this article, we propose a novel solution, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">graph-augmented social translation model</i> (GAST), which investigates the utility of dynamic social influence for the task of next-item recommendation. Specifically, we introduce a gated graph convolution module to better model long-term user preference. Furthermore, we design a cogating module to capture dynamic patterns at both sequential level and social level. In addition, a social-enhanced translation mechanism is devised to measure the intensity of user–item relationships. Extensive experiments under different recommendation scenarios demonstrate the rationality and effectiveness of our proposed GAST method over several state-of-the-art methods.