融合多维梯度反馈的生成对抗网络推荐系统

Generative Adversarial Network Recommendation System with Multi-dimensional Gradient Feedback Mechanism

互联网时代,推荐系统在日常生活中变得十分重要,生成对抗网络(GAN)与推荐算法的结合为该领域的发展提供了新契机。以往基于生成对抗网络的推荐系统中,鉴别器提供的梯度反馈是二元的,此类反馈为生成器提供的帮助不够全面,造成诸如生成器性能不稳定、迭代速度慢等问题,进而影响模型的整体推荐效果。针对此问题,提出了多维梯度反馈生成对抗网络(MGFGAN),根据生成器生成的多维用户评分向量,该模型将自编码器(AutoEncoder)融入鉴别器中,达到为生成器提供多元反馈的目的,旨在让生成器生成的数据更加贴近用户偏好;此外,融合多维梯度反馈机制给模型整体带来了运算量激增的问题;因此,MGFGAN在生成器中引入了负采样模块,使得生成器同时兼顾用户感兴趣和不感兴趣的物品,从而加速生成器快速生成与真实用户分布一致的数据,提升模型的效率。提出的模型在公开数据集FilmTrust和Ciaos上展开实验仿真。实验结果表明MGFGAN的推荐性能优于其他基于生成对抗网络的推荐模型,并且在效率和稳定性方面取得改善。

In the Internet era,recommender systems become more and more significant in the daily life.The combination of generative adversarial networks(GAN)and recommended algorithm provides new opportunities for the development of this field.In previous recommendation systems based on GAN,the gradient feedback provided by the discriminator is binary,which does not comprehensively assist the generator.This inadequacy leads to issues such as unstable generator performance and slow model iteration speed,thereby reducing the overall effectiveness of recommendations.Multi-dimensional gradient feedback generative adversarial networks(MGFGAN)is proposed to address above problems.According to the type of generated multidimensional user rating vector,the model incorporates AutoEncoder in the discriminator to provide more diversified feedback for the generator,aiming to make the generated data more closely match the user’s preferences of the model.However,it brings the problem of increasing computational complexity to the model.Therefore,MGFGAN introduces a negative sampling module in the generator,which makes the generator take into account both items of interest and disinterest to the user,thus accelerating the generator to quickly generate data consistent with the real user distribution and improving the efficiency of the model.Finally,the MGFGAN is carried out experimental simulation on the public datasets FilmTrust and Ciaos.Experimental results show that the recommendation performance of MGFGAN outperforms other recommendation models based on GAN and achieves improvements in efficiency and stability.