The Interaction Graph Auto-encoder Network Based on Topology-aware for Transferable Recommendation

Deep learning-based recommendation systems have made significant strides in recent years. However, the problem of recommendation systems' generalizability has not been solved. After the training phase, most current models can only solve problems on a particular dataset and are not as generalizable as NLP and CV models. Therefore, a large amount of computing power is required to make conventional recommendation models available to different trades. In real-world scenarios, offline retailers often opt out of recommendation algorithms due to a lack of computer capacity, which puts them at a competitive disadvantage. As a result, we propose an Interaction Graph Auto-encoder Network (IGA) based on topology-aware to address the transferable recommendation problem. IGA is composed primarily of the following components: Interaction Feature Subgraph Extraction, Subgraph Node Labeling, Subgraph Interaction Auto-encoder, and Interaction Preference Attention Network. IGA can transfer knowledge from the training dataset to the new dataset without fine-tuning and give users reliable, personalized recommendation results. Experiments on the MovieLens, Douban, LastFM, and Book-Crossing datasets demonstrate that IGA outperforms state-of-the-art approaches in transferable scenarios. Additionally, IGA requires fewer computing power and is highly adaptable across datasets.