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

TL;DR

This paper introduces CnGAN, a multi-task learning framework that generates user preferences for non-overlapped users across networks, improving cross-network recommendation accuracy, novelty, and diversity.

Contribution

We propose CnGAN, a novel encoder-GAN architecture with a pairwise loss function for generating user preferences for non-overlapped users in cross-network recommendations.

Findings

Generated preferences improve recommendation accuracy.

The approach outperforms state-of-the-art solutions.

Enhanced diversity and novelty in recommendations.

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.