华东师范大学学报（自然科学版） >

2024 , Vol. 2024 >Issue 2: 53 - 64

DOI: https://doi.org/10.3969/j.issn.1000-5641.2024.02.007

计算机科学

基于偏好级别陌生人信息辅助的推荐系统

  • 冯路平 ,
  • 施力业 ,
  • 吴雯 ,
  • 郑骏 ,
  • 胡文心 ,
  • 郑巍
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  • 1. 华东师范大学 数据科学与工程学院, 上海　200062
    2. 华东师范大学 计算机科学与技术学院,上海　200062
    3. 华东师范大学 信息化治理办公室, 上海　200062

收稿日期: 2022-11-01

  网络出版日期: 2024-03-18

基金资助

国家自然科学基金 (61907016); 上海市科委项目 (21511100302)

收起

Collaborative stranger review-based recommendation

  • Luping FENG ,
  • Liye SHI ,
  • Wen WU ,
  • Jun ZHENG ,
  • Wenxin HU ,
  • Wei ZHENG
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  • 1. School of Data Science and Engineering, East China Normal University, Shanghai　200062, China
    2. School of Computer Science and Technology, East China Normal University, Shanghai　200062, China
    3. Information Technology Governance Office, East China Normal University, Shanghai　200062, China

Received date: 2022-11-01

  Online published: 2024-03-18

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摘要

基于评论的推荐系统是一种主要通过挖掘文本信息抽取物品特征和用户偏好, 以提高性能的推荐系统方法. 现存的大多数方法忽略了撰写评论的陌生人信息, 引入陌生人信息可以更准确地衡量用户的相对感受并对目标用户的表达进行补充, 从而进行更精细的用户建模. 近年来, 一些研究尝试整合相似陌生人的信息, 但忽略了对其他陌生人信息的利用. 提出了基于偏好级别陌生人信息辅助的推荐系统模型CSRR(collaborative stranger review-based recommendation), 利用陌生人信息, 更加准确地对用户建模并进行适当扩展, 提升了推荐性能. 具体地, 为了准确捕捉用户的偏好, 首先, 设计了一个基于陌生人信息辅助的注意力模块, 该模块不仅考虑了评论文本的相似性, 也考虑了目标用户与撰写评论的陌生人之间的偏好交互作用; 其次, 一个基于陌生人信息过滤的门控模块根据目标用户–物品对的特征, 在偏好级别动态整合陌生人信息, 有效地过滤了陌生人的偏好信息以及丰富目标用户的建模; 最后, 应用隐因子模型 (latent factor model, LFM) 来完成评分预测任务. 实验结果说明CSRR模型在多个来源的真实数据集上均具有较高的预测准确度.

关键词： 基于评论的推荐系统; 陌生人信息; 注意力机制; 门控机制

本文引用格式

冯路平 , 施力业 , 吴雯 , 郑骏 , 胡文心 , 郑巍 . 基于偏好级别陌生人信息辅助的推荐系统[J]. 华东师范大学学报（自然科学版）, 2024 , 2024(2) : 53 -64 . DOI: 10.3969/j.issn.1000-5641.2024.02.007

Abstract

Review-based recommendations are mainly based on the exploitation of textual information that reflects the characteristics of items and user preferences. However, most existing approaches overlook the influence of information from hidden strangers on the selection of reviews for the target user. However, information from strangers can more accurately measure the relative feelings of the user and provide a complement to the target user’s expression, leading to more refined user modeling. Recently, several studies have attempted to incorporate similar information from strangers but ignore the use of information regarding other strangers. In this study, we proposed a stranger collaborative review-based recommendation model to make effective use of information from strangers by improving accurate modeling and enriching user modeling. Specifically, for capturing potential user preferences elaborately, we first designed a collaborative stranger attention module considering the textual similarities and preference interactions between the target user and the hidden strangers implied by the reviews. We then developed a collaborative gating module to dynamically integrate information from strangers at the preference level based on the characteristics of the target user-item pair, effectively filtering preferences of strangers and enriching target user modeling. Finally, we applied a latent factor model to accomplish the recommendation task. Experimental results have demonstrated the superiority of our model compared to state-of-the-art methods on real-world datasets from various sources.

Key words： review-based recommendation; stranger information; attention mechanism; gating mechanism

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