An intelligent recommendation method based on multi-interest network and adversarial deep learning

Recommender systems have shown to popular in many Internet communities, as they could help users discover interesting items based on their history behaviors. However, with the explosive growth of data-intensive tasks and online information, cybersecurity risks become larger, conventional collaborative recommendation algorithms may not meet users’ security requirements. Besides, the sparsity issue and the cold-start issue also hinder the performance of conventional recommendation methods. Recently, deep learning has shown to outperform traditional modeling techniques, which can be employed in Recommender systems (RSs) to improve user behavior prediction. In light of these challenges and observations, an intelligent recommendation method based on multi-interest network and adversarial deep learning is proposed, where multi-source behavior information is applied for multi-view embedding extraction for better prediction performance. Specifically, multi-view preference embeddings, including self-embedding, interaction-aware embedding, and neighbor-based embedding, are combined to model users’ interests at a finer granularity. Besides, in neighbor-based embedding learning, an adversarial search scheme is adopted for fast similarity searching and privacy preservation. Finally, a DNN-based prediction mechanism is adopted for embedding aggregation and final prediction. Extensive experiments on real-world datasets show that our proposal achieves decent prediction performance with security concerns compared with state-of the-art baselines.