基于双向条目注意网络的推荐系统

赵永建; 杨振国; 刘文印

doi:10.12052/gdutxb.200002

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基于双向条目注意网络的推荐系统

更新时间：2026-05-27

- 基于双向条目注意网络的推荐系统
- DIAN: Dual-aspect Item Attention Network for Item-based Recommendation
- 广东工业大学学报 2020年37卷第4期页码：27-34
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
  
  赵永建(1994–)，男，硕士研究生，主要研究方向为机器学习、推荐系统
  刘文印(1966–)，男，教授，博师生导师，主要研究方向为机器学习、区块链、网络身份安全等，E-mail：liuwy@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61703109, 91748107);广东省引进创新科研团队计划资助项目(2014ZT05G157)
- DOI：10.12052/gdutxb.200002
  中图分类号： TP391
- 网络首发：2020-07-11，
  
  纸质出版：2020
- 稿件说明：
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赵永建, 杨振国, 刘文印. 基于双向条目注意网络的推荐系统[J]. 广东工业大学学报, 2020, 37(4): 27-34.

Zhao Yong-jian, Yang Zhen-guo, Liu Wen-yin. DIAN: Dual-aspect Item Attention Network for Item-based Recommendation[J]. 2020, 37(4): 27-34.
赵永建, 杨振国, 刘文印. 基于双向条目注意网络的推荐系统[J]. 广东工业大学学报, 2020, 37(4): 27-34. DOI： 10.12052/gdutxb.200002.

Zhao Yong-jian, Yang Zhen-guo, Liu Wen-yin. DIAN: Dual-aspect Item Attention Network for Item-based Recommendation[J]. 2020, 37(4): 27-34. DOI： 10.12052/gdutxb.200002.

摘要

提出了一个基于双向条目注意网络(Dual-aspect Item Attention Network

DIAN)的推荐系统

DIAN由2个主要模块组成

分别是用于建模历史条目和目标条目之间的条目相似性的神经关注模型(Neural Attentive Item Similarity Model

NAIS)和用于建模历史条目之间相似性的双归一化自注意力条目相似性模型(Dual Normalization Self-attention Item Similarity

SAIS)。一方面

引入神经注意模型来区分用户配置文件中历史项对目标项的影响。另一方面

为了更好地表达用户的兴趣

引入自注意力网络

从用户的历史交互项中推断出条目与条目之间的关系。提出的SAIS模型能够估计用户交互轨迹中每个条目对用户兴趣的相对权重。用双重归一化机制改进了标准的自注意力网络

并且在2个公共基准上进行大量实验证明所提出的方法优于最先进的推荐模型。

Abstract

A dual-aspect item attention network (DIAN) for item-based recommendation is proposed

which jointly takes into account the aspects of importance of historical items in a user profile to the target items and the underlying relations among these items. DIAN consists of two main modules

a neural attentive model for item similarity between historical and target items (NAIS)

and a dual normalization self-attention item similarity model for item similarity underlying historical items (SAIS). On one hand

the neural attentive model is introduced to distinguish the different contribution of the historical items in a user profile to the perdition on the target item. On the other hand

a self-attention network is proposed to infer the item-item relationship from users’ historical interactions

which is able to estimate the relative weights of each item in user interaction trajectories

in order to learn better representations for users’ interests. Furthermore

a self-attention network is proposed using a dual normalization mechanism

consisting of a layer focusing on extracting users’ representation from historical items

and a layer making it unaffected by the number of users’ historical items. Extensive experiments conducted on two public benchmarks demonstrate the proposed method outperforms the state-of-the-art recommendation models.

关键词

Keywords

references

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