ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (8): 1697-1706.doi: 10.7544/issn1000-1239.2020.20200497

所属专题: 2020数据挖掘与知识发现专题

• 人工智能 • 上一篇    下一篇

基于列表级排序的深度生成推荐方法

孙肖依,刘华锋,景丽萍,于剑   

  1. (交通数据分析与挖掘北京市重点实验室(北京交通大学) 北京 100044) (北京交通大学计算机与信息技术学院 北京 100044) (sunxiaoyi@bjtu.edu.cn)
  • 出版日期: 2020-08-01
  • 基金资助: 
    国家自然科学基金项目(61822601,61773050,61632004);北京市自然科学基金项目(Z180006);北京市科委项目(Z181100008918012);国家重点研发计划项目(2017YFC1703506);中央高校基本科研业务费专项资金(2019JBZ110)

Deep Generative Recommendation Based on List-Wise Ranking

Sun Xiaoyi, Liu Huafeng, Jing Liping, Yu Jian   

  1. (Beijing Key Laboratory of Traffic Data Analysis and Mining (Beijing Jiaotong University), Beijing 100044) (School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044)
  • Online: 2020-08-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61822601, 61773050, 61632004), the Beijing Natural Science Foundation (Z180006), the Program of Beijing Municipal Science & Technology Commission (Z181100008918012), the National Key Research and Development Program of China (2017YFC1703506), and the Fundamental Research Funds for the Central Universities (2019JBZ110).

摘要: 变分自编码器(variational autoencoder, VAE)近年来在推荐领域有着很成功的应用.这种非线性概率模型的优势在于它可以突破线性模型有限的建模能力,而线性模型目前仍然在协同过滤研究中占主导地位.尽管基于变分自编码器的推荐方法已经取得了优越的表现,但仍存在一些未解决的问题,例如无法针对隐式反馈的推荐数据为用户生成个性化的推荐排序列表.因此,通过借助多项式似然对变分自编码器实施基于列表的排序策略,提出了一种深度生成推荐模型.该模型具有同时生成点级隐式反馈数据并为每个用户创建列表式偏好排序的能力.为了将排序损失与变分自编码器损失结合起来,采取归一化累计损失增益(normalized cumulative loss gain, NDCG)作为排名损失,并通过平滑函数进行近似.在3个真实世界数据集上(MovieLens-100k,XuetangX和Jester)进行了实验.实验结果表明:结合了列表级排序的变分自编码器在推荐个性化列表所有评价指标上,相比于其他基线模型拥有更出色的表现.

关键词: 个性化推荐, 深度生成模型, 排序学习, 隐式反馈, 列表级排序

Abstract: Variational autoencoders have been successfully applied in recommendation field in recent years. The advantage of this kind of nonlinear probabilistic model is that it can break through the limited modeling ability of linear model, which is still dominant in collaborative filtering research. Although the recommendation method based on variational autoencoder has achieved excellent performance, there are still some unresolved problems, such as the inability to generate personalized recommendation ranking lists for users based on the recommendation data of implicit feedback. Therefore, in this paper, we propose a depth generation recommendation model for variational autoencoder by using polynomial likelihood to implement list-based ranking strategies. The model has the ability to simultaneously generate point-wise implicit feedback data and create a list-like ranking list for each user. To seamlessly combine ranking loss with variational autoencoder loss, the normalized cumulative loss gain (NDCG) is adopted here and approximated with a smoothed function. A series of experiments on three real-world datasets (MovieLens-100k, XuetangX and Jester) have been conducted. Experimental results show that the variational autoencoder combined with list-wise ranking method has better performance in generate a personalized recommendation list.

Key words: personalized recommendation, deep generative model, learning to rank, implicit feedback, list-wise ranking

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