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The recent popularity of edge devices and Artificial Intelligent of Things(AIoT) has driven a new wave of contextual recommendations, such as locationbased Point of Interest (PoI) recommendations and computing resource-awaremobile app recommendations. In many such recommendation scenarios, contexts aredrifting over time. For example, in a mobile game recommendation, contextualfeatures like locations, battery, and storage levels of mobile devices arefrequently drifting over time. However, most existing graph-based collaborativefiltering methods are designed under the assumption of static features.Therefore, they would require frequent retraining and/or yield graphical modelsburgeoning in sizes, impeding their suitability for context-driftingrecommendations.  In this work, we propose a specifically tailor-made Hybrid Static andAdaptive Graph Embedding (HySAGE) network for context-drifting recommendations.Our key idea is to disentangle the relatively static user-item interaction andrapidly drifting contextual features. Specifically, our proposed HySAGE networklearns a relatively static graph embedding from user-item interaction and anadaptive embedding from drifting contextual features. These embeddings areincorporated into an interest network to generate the user interest in somecertain context. We adopt an interactive attention module to learn theinteractions among static graph embeddings, adaptive contextual embeddings, anduser interest, helping to achieve a better final representation. Extensiveexperiments on real-world datasets demonstrate that HySAGE significantlyimproves the performance of the existing state-of-the-art recommendationalgorithms.

HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations