LambdaGAN: Generative Adversarial Nets for Recommendation Task with Lambda Strategy

Abstract

The Top-N recommendation task aims to recommend users the items they like most. Generative Adversarial Net (GAN) has achieved good results on recommendation, which learns user-item matrix by a generative adversarial training process. There are two main scenarios, pointwise scenarios and pairwise scenarios. However, for recommendation, GANs in pairwise scenarios perform not as well as these in pointwise scenarios. As pairwise rank is a position-independent algorithm, it does not consider Top-N ranking sufficiently. Recommendation task is position-dependent. Especially the Top-N item ranking accuracy is much more important than the ranking accuracy of the tail item. In this paper, we propose LambdaGAN for Top-N recommendation. LambdaGAN introduces lambda rank into generative adversarial training process in order to consider the ranking information of the item. The proposed model enables generative adversarial training in pairwise scenarios available for recommendation by optimizing the rank based metrics directly. Moreover, we adjust lambda function according to the characteristics of recommendation. Two new designed lambda functions are proposed. Experimental results show that LambdaGAN outperforms state-of-the-art algorithms including BPR, PRFM, LambdaFM and IRGAN in terms of four standard evaluation metrics on two widely used datasets, Movielens-100K and Netflix.