Spatiotemporal-view member preference contrastive representation learning for group recommendation

Yangtao Zhou, Qingshan Li, Hua Chu, Jianan Li, Biaobiao Wei, Shuai Zhang, Jialong Han

Abstract: Group recommendation (GR) plays a crucial role in social platforms, aiming to recommend items to entire groups based on collective interaction behaviors. Existing GR models predominantly focus on aggregating information from the view of spatial graph structure to infer preferences of members and groups. However, these models neglect the temporal relations among interactions that indicate the dynamic evolution of preferences, and fail to further integrate both the spatial and temporal features. To address these limitations, we propose a novel model called SpatioTemporal-view Member Preference Contrastive Representation Learning (STMP-CRL) for GR. The STMP-CRL can explicitly capture dynamic member preferences in the temporal view and seamlessly integrate them with spatial features to enhance member representation quality. Specifically, a GRU-based context dynamic encoder is proposed for the temporal view modeling to capture the dynamic member preferences. Additionally, to co-model member preferences in both the spatial and temporal views, a spatiotemporal-view joint encoding module is carefully devised. Furthermore, we propose a contrastive fusion mechanism based on independence modeling, which effectively integrates the spatial and temporal features via a disentangle-and-fuse strategy, enhancing the overall quality of member representations. Experimental results on two real datasets showcase the superiority of our STMP-CRL model over mainstream GR models, as evidenced by notable improvements in HR and NDCG metrics. Our implementations are available at https://github.com/STMP-CRL/STMP-CRL.