计算机科学 ›› 2019, Vol. 46 ›› Issue (8): 111-115.doi: 10.11896/j.issn.1002-137X.2019.08.018

• 2018 全国高性能计算学术年会 • 上一篇    下一篇

基于用户向量化表示和注意力机制的深度神经网络推荐模型

郭旭1, 朱敬华1,2   

  1. (黑龙江大学计算机科学技术学院 哈尔滨150080)1
    (黑龙江省数据库与并行计算重点实验室 哈尔滨150080)2
  • 收稿日期:2018-10-21 出版日期:2019-08-15 发布日期:2019-08-15
  • 通讯作者: 朱敬华(1976-),男,教授,CCF会员,主要研究方向为数据挖掘、推荐系统,E-mail:zhujinghua@hlju.edu.cn
  • 作者简介:郭旭(1989-),男,硕士,主要研究方向为数据挖掘
  • 基金资助:
    黑龙江省教育厅科研项目(12531498)

Deep Neural Network Recommendation Model Based on User Vectorization Representation and Attention Mechanism

GUO Xu1, ZHU Jing-hua1,2   

  1. (School of Computer Science and Technology,Heilongjiang University,Harbin 150080,China)1
    (Key Laboratory of Database and Parallel Computing of Heilongjiang Province,Harbin 150080,China)2
  • Received:2018-10-21 Online:2019-08-15 Published:2019-08-15

摘要: 随着互联网应用的蓬勃发展,推荐系统作为解决信息过载的有效手段,成为了工业界与学术界的研究热点。面向用户隐式反馈的传统推荐算法主要基于协同过滤和排序学习等方法,但这些方法未充分利用用户行为中的隐式反馈特征。文中提出了一种基于神经网络的用户向量化表示模型,其能够充分利用用户的异构的隐式反馈行为特征。同时,借鉴机器翻译中的self-attention机制,设计了一种神经注意力推荐模型,其融合用户向量化表示和用户-项目交互的动态时序特征以提高推荐系统的性能。在公开数据集上进行对比实验,通过召回率、准确率、NDCG 3个指标评价推荐性能。结果表明,与其他面向隐式反馈的推荐模型相比,所提推荐模型具有更好的推荐性能,并且对用户行为特征具有很好的泛化能力。

关键词: 推荐系统, 神经网络, 注意力机制, 隐式反馈, 深度学习

Abstract: With the rapid development of Internet application,recommendation system,as an effective measure to solve information overloading,has become a research hot topic in industry and academia.Traditional recommendation algorithms for users’ implicit feedback are mainly based on collaborative filtering and learning-to-rank method,which do not make full use of the implicit feedback features in users’ behaviors.In this paper,a users’ vectorization representation model based on neural network was proposed,which can make full use of heterogeneous implicit feedback features of users’ behaviors.At the same time,referring to the self-attention mechanism in machine translation,this paper designed a neural attentive recommendation model which integrates the dynamic temporal features of user-item interaction and user vectorization representation,to improve the performance of the recommendation system.The comparison experiment is conducted on two public datasets,and the recommendation performance is evaluated by recall,precision and NDCG.Compared with other recommendation models for implicit feedback,the proposed recommendation model has better recommendation performance and better generalization ability

Key words: Recommendation system, Neural networks, Attention mechanism, Implicit feedback, Deep learning

中图分类号: 

  • TP391
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