Neural Memory Streaming Recommender Networks With Adversarial Training

Authors:
Qinyong Wang The University of Queensland
Hongzhi Yin The University of Queensland
Zhiting Hu Language Technologies Institute, Carnegie Mellon University
Defu Lian School of Computer Science and Engineering, University of Electronic Science and Technology of China
Hao Wang 360 Search Lab
Zi Huang The University of Queensland

Introduction:

This paper studies ecommender systems with inputs of streaming data. The authors propose a streaming recommender model based on neural memory networks.An adaptive negative sampling framework based on Generative Adversarial Nets (GAN) is developed to optimize the paper's proposed streaming recommender model.

Abstract:

With the increasing popularity of various social media and E-commerce platforms, large volumes of user behaviour data (e.g., user transaction data, rating and review data) are being continually generated at unprecedented and ever-increasing scales. It is more realistic and practical to study recommender systems with inputs of streaming data. User-generated streaming data presents unique properties such as temporally ordered, continuous and high-velocity, which poses tremendous new challenges for the once very successful recommendation techniques. Although a few temporal or sequential recommender models have recently been developed based on recurrent neural models, most of them can only be applied to the session-based recommendation scenario, due to their short-term memories and the limited capability of capturing users’ long-term stable interests. In this paper, we propose a streaming recommender model based on neural memory networks with external memories to capture and store both long-term stable interests and short-term dynamic interests in a unified way. An adaptive negative sampling framework based on Generative Adversarial Nets (GAN) is developed to optimize our proposed streaming recommender model, which effectively overcomes the limitations of classical negative sampling approaches and improves both effectiveness and efficiency of the model parameter inference. Extensive experiments have been conducted on two large-scale recommendation datasets, and the experimental results show the superiority of our proposed streaming recommender model in the streaming recommendation scenario.

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