Adversarial Path Sampling for Recommender Systems

Abstract

Generative Adversarial Networks(GANs) have achieved a big success in collaborative ltering(CF). However, existing GAN-based methods in CF still suffer from the high-sparsity and cold-start problems; in addition, they also undergo the issues of excessive space complexity or inadequate training. In this paper, we propose path2rec, a novel adversarial path-based recommendation model to address these limitations of existing GAN-based methods in recommendation task by naturally incorporating auxiliary information(e.g., social networks and item attributes). It is composed of two models, pathGAN and path2vec. In pathGAN, we consider both explicit and implicit friends, as well as item attributes by regarding them as the source of graph construction. Then we propose a tree pruning sampling strategy to automatically generate an optimizing path, which can effectively learn the semantic distribution of users and items. In path2vec, to fully exploit context features of the generated path, we use CBOW to ne-tune nodes representations learned by pathGAN. Through extensive experiments on real-world datasets, we demonstrate the effectiveness of the proposed path2rec by applying it into top-n item recommendation, which reaches better performance than other counterparts.