基于方面和胶囊网络的跨域评分预测模型

梁顺攀; 刘伟; 郑智中; 原福永

doi:10.12263/DZXB.20210483

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基于方面和胶囊网络的跨域评分预测模型

学术论文 | 更新时间：2025-12-08

- 基于方面和胶囊网络的跨域评分预测模型
- Cross Domain Rating Prediction Based on Aspect and Capsule Network
- 电子学报 2022年50卷第9期页码：2102-2109
- 作者机构：
  
  燕山大学信息科学与工程学院, 河北秦皇岛 066004
- 作者简介：
  
  [ "梁顺攀男, 1976年6月出生于广东省江门市, 现为燕山大学副教授、硕士生导师, 研究方向为推荐系统.在国内外发表学术论文20余篇." ]
- 基金信息：
  
  河北省自然科学基金(G2021203010;F2021203038)
- DOI：10.12263/DZXB.20210483
  中图分类号： TP391
- 收稿：2021-04-15，
  
  修回：2021-11-08，
  
  纸质出版：2022-09-25
- 稿件说明：
移动端阅览
梁顺攀,刘伟,郑智中等.基于方面和胶囊网络的跨域评分预测模型[J].电子学报,2022,50(09):2102-2109.

LIANG Shun-pan,LIU Wei,ZHENG Zhi-zhong,et al.Cross Domain Rating Prediction Based on Aspect and Capsule Network[J].ACTA ELECTRONICA SINICA,2022,50(09):2102-2109.
梁顺攀,刘伟,郑智中等.基于方面和胶囊网络的跨域评分预测模型[J].电子学报,2022,50(09):2102-2109. DOI： 10.12263/DZXB.20210483.

LIANG Shun-pan,LIU Wei,ZHENG Zhi-zhong,et al.Cross Domain Rating Prediction Based on Aspect and Capsule Network[J].ACTA ELECTRONICA SINICA,2022,50(09):2102-2109. DOI： 10.12263/DZXB.20210483.

摘要

用户评论可以反映用户对项目的偏好信息，将用户在其他领域的偏好迁移到目标领域进行跨域推荐，可以缓解目标域数据稀疏引起的冷启动问题.本文针对传统的跨域推荐方法无法将完整的用户偏好进行迁移以及传统的方面提取方法预测精度不高两个问题，提出基于方面和胶囊网络的跨域评分预测模型ACN（Aspect and Capsule Network）.ACN模型使用胶囊网络挖掘评论文档的多个方面，然后通过注意力机制筛选出对目标域最重要的特征，迁移到目标域进行评分预测.最后，通过实验证明ACN模型分别在单一源域和多源域的情况下，较基准模型最高有2.3%和20.8%的性能提升.

Abstract

Users'reviews on items can reflect users'preference information. Migrating users'preferences in other fields to the target domain for cross domain recommendation can alleviate the cold start problem caused by sparse data in the target domain. Aiming at the two problems that the traditional cross domain recommendation methods can not migrate the complete user preferences and the prediction accuracy of the traditional aspect extraction methods is incorrect

this paper proposes a cross domain score prediction model based on aspect and capsule network(ACN). ACN uses capsule network to mine multiple aspects of review documents

and then selects the most important features for the target domain through attention mechanism

and migrate to the target domain for score prediction. Compared with the benchmark model

ACN has the highest performance improvement of 2.3% and 20.8% when using single source domain and multi-source domain.

关键词

Keywords

references

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