CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped users

Abstract

A major drawback of cross-network recommender solutions is that they can only be applied to users that are overlapped across networks. Thus, the non-overlapped users, which form the majority of users are ignored. As a solution, we propose CnGAN, a novel multi-task learning based, encoder-GAN-recommender architecture. The proposed model synthetically generates source network user preferences for non-overlapped users by learning the mapping from target to source network preference manifolds. The resultant user preferences are used in a Siamese network based neural recommender architecture. Furthermore, we propose a novel user based pairwise loss function for recommendations using implicit interactions to better guide the generation process in the multi-task learning environment.We illustrate our solution by generating user preferences on the Twitter source network for recommendations on the YouTube target network. Extensive experiments show that the generated preferences can be used to improve recommendations for non-overlapped users. The resultant recommendations achieve superior performance compared to the state-of-the-art cross-network recommender solutions in terms of accuracy, novelty and diversity.

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• Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
• Cross-Market Product Recommendation
• A survey on Adversarial Recommender Systems: from Attack/Defense strategies to Generative Adversarial Networks
• CATA++: A Collaborative Dual Attentive Autoencoder Method for Recommending Scientific Articles
• Semantic-enhanced Co-attention Prompt Learning for Non-overlapping Cross-Domain Recommendation
• Market-Aware Models for Efficient Cross-Market Recommendation