计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (4): 119-128.doi: 10.11896/jsjkx.240700053

• 数据库&大数据&数据科学 • 上一篇    下一篇

融合词间句间多关系建模的评论推荐算法

邓策渝1, 李段腾川1,2, 胡奕仁2, 王晓光1, 李志飞3   

  1. 1 武汉大学信息管理学院 武汉 430072
    2 武汉大学计算机学院 武汉 430072
    3 湖北大学计算机与信息工程学院 武汉 430062
  • 收稿日期:2024-07-08 修回日期:2024-11-04 出版日期:2025-04-15 发布日期:2025-04-14
  • 通讯作者: 李段腾川(dtclee1222@whu.edu.cn)
  • 作者简介:(ceyudeng@163.com)
  • 基金资助:
    国家社会科学基金重大项目(21&ZD334);国家自然科学基金(62207011)

Joint Inter-word and Inter-sentence Multi-relationship Modeling for Review-basedRecommendation Algorithm

DENG Ceyu1, LI Duantengchuan1,2, HU Yiren2, WANG Xiaoguang1, LI Zhifei3   

  1. 1 School of Information Management,Wuhan University,Wuhan 430072,China
    2 School of Computer Science,Wuhan University,Wuhan 430072,China
    3 School of Computer and Information Engineering,Hubei University,Wuhan 430062,China
  • Received:2024-07-08 Revised:2024-11-04 Online:2025-04-15 Published:2025-04-14
  • About author:DENG Ceyu,born in 2002,postgraduate,is a student member of CCF(No.P0293G).His main research interests include recommender system and natural language processing.
    LI Duantengchuan,born in 1994,Ph.D,is a member of CCF(No.J0537G).His main research interests include recommender system,knowledge graph and computer vision.
  • Supported by:
    Major Project of the National Social Science Fundation of China(21&ZD334) and National Natural Science Foundation of China(62207011).

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摘要： 评论作为最常见的辅助信息,能够直观地表达用户的喜好和物品的属性,被研究者们广泛应用于优化推荐算法的预测精度。然而,目前评论推荐算法仍存在不足,主要体现在忽略了对评论文本多粒度特征的建模和对用户偏好、物品属性这一对异质特征的关联性交互学习,导致模型无法充分提取评论信息,影响了模型精度。因此,提出了融合词间句间多关系建模的评论推荐算法(MR4R),通过分析评论文本的词间重要性关系和句间时序动态关系,抽取不同层次的特征信息;设计了融合预测层,对用户偏好与物品属性特征间的关联性挖掘过程进行优化,并通过高阶非线性计算进行评分预测。选取了4个不同场景的数据集,并将所提模型与目前主流的7种推荐算法进行比较。实验结果表明,融合词间句间多关系建模的推荐算法能够充分提取评论中蕴含的信息,显著提升了平均推荐精度,具有更强的推荐性能。

关键词: 评论推荐算法, 深度学习, 多关系建模, 时序特征建模, 异质特征融合与交互

Abstract: Reviews,a prevalent form of auxiliary information,directly reflect user preferences and item characteristics,extensively utilized by researchers to enhance the predictive accuracy of recommendation algorithms.However,the current review recommendation algorithm still has shortcomings,which are mainly reflected in the fact that the existing model ignores the modeling of multi-granularity feature extraction of review text and the relational interactive learning of user preferences and item attributes,a pair of heterogeneous features.This oversight leads to insufficient extraction of review information,compromising model accuracy.Thus,joint inter-word and inter-sentence multi-relationship modeling for review-based recommendation algorithm(MR4R) is introduced in the study.Firstly,multi-relational modeling strategy is adopted to analyze inter-word and inter-sentence relationships in review texts to extract layered feature information,thereby enriching the model’s grasp of user preferences and refining item attribute representations.The fusion and prediction layer is designed to optimize the correlation mining process between user preferences and item attributes,and the score prediction is carried out by high order nonlinear calculation.The proposed model is compared with seven current mainstream recommendation algorithms on four distinct datasets.The results demonstrate that the recommendation algorithm,which incorporates multi-relational modeling between words and sentences,effectively extracts information embedded in reviews,significantly enhancing average recommendation accuracy and exhibiting superior performance.

Key words: Review-based recommendation algorithm, Deep learning, Multi-relation modeling, Temporal feature modeling, Heterogeneous feature fusion and interaction

中图分类号: 

  • TP391.3
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