Interaction Subgraph Sequential Topology-Aware Network for Transferable Recommendation

Recommendation systems have primarily been limited to research on a single dataset compared to natural language processing and computer vision, which have seen tremendous growth in transferable tasks. Existing approaches for recommendation systems need to be more scalable to arbitrary tasks, given that previous research efforts on transferable recommendations have only yielded brief explorations and neglected systematic studies of sequential tasks. In this regard, we propose the interaction subgraph sequential topology-aware network (ISTN), which overcomes this limitation, enabling transferable sequence recommendations. ISTN performs subgraph sampling and node labeling of user interactions, captures the topological features of the user interaction sequences with the sequential topology auto-encoder, and employs the sequential preference decoupling module to decouple user interaction sequences for transferable adaptive granularity modeling of user preferences. ISTN requires no fine-tuning, and its knowledge transfer capability from the training dataset to the new dataset delivers accurate, individualized recommendation results. ISTN outperforms state-of-the-art performance in transferable contexts with only minor performance degradation compared to the traditional baseline, as shown in Yelp, MovieLens, and Foursquare experiments.