AMT-CDR: A Deep Adversarial Multi-channel Transfer Network for Cross-domain Recommendation

Abstract

Recommender systems are one of the most successful applications of using AI for providing personalized e-services to customers. However, data sparsity is presenting enormous challenges that are hindering the further development of advanced recommender systems. Although cross-domain recommendation partly overcomes data sparsity by transferring knowledge from a source domain with relatively dense data to augment data in the target domain, the current methods do not handle heterogeneous data very well. For example, using today’s cross-domain transfer learning schemes with data comprising clicks, ratings, user reviews, item meta data, and knowledge graphs will likely result in a poorly-performing model. User preferences will not be comprehensively profiled, and accurate recommendations will not be generated. To solve these three challenges – i.e., handling heterogeneous data, avoiding negative transfer, and dealing with data sparsity – we designed a new end-to-end deep a dversarial m ulti-channel t ransfer network for c ross- d omain r ecommendation named AMT-CDR. Heterogeneous data is handled by constructing a cross-domain graph based on real-world knowledge graphs – we used Freebase and YAGO. Negative transfer is prevented through an adversarial learning strategy that maintains consistency across the different data channels. And data sparsity is addressed with an end-to-end neural network that considers data across multiple channels and generates accurate recommendations by leveraging knowledge from both the source and target domains. Extensive experiments on three dual-target cross-domain recommendation tasks demonstrate the superiority of AMT-CDR compared to eight state-of-the-art methods. All source code is available at https://github.com/bjtu-lucas-nlp/AMT-CDR.