一种改进的结合评分和评论信息的推荐方法

张巍; 黄健华; 刘冬宁; 滕少华; 刘子婷

doi:10.12052/gdutxb.170014

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一种改进的结合评分和评论信息的推荐方法

综合研究 | 更新时间：2026-05-27

- 一种改进的结合评分和评论信息的推荐方法
- An Improved Recommendation Method Using Rating and Review Information
- 广东工业大学学报 2017年34卷第6期页码：27-31
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 哈尔滨师范大学数学科学学院,黑龙江,哈尔滨,150025
- 作者简介：
  
  张巍(1964–)，女，副教授，主要研究方向为协同计算、数据挖掘、网络安全、大数据. E-mail：weizhang@gdut.edu.cn
  刘冬宁(1979–)，男，副教授，博士，主要研究方向为人工智能逻辑、数据库与协同计算. E-mail：liudn@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61402118, 61673123);广东省科技计划项目(2015B090901016, 2016B010108007);广东省教育厅项目(粤教高函2015[133]号, 粤教高函〔2014〕97号);广州市科技计划项目(201604020145, 2016201604030034, 201508010067)
- DOI：10.12052/gdutxb.170014
  中图分类号： TP181
- 网络首发：2017-11-09，
  
  纸质出版：2017
- 稿件说明：
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张巍, 黄健华, 刘冬宁, 滕少华, 刘子婷. 一种改进的结合评分和评论信息的推荐方法[J]. 广东工业大学学报, 2017, 34(6): 27-31.

Zhang Wei, Huang Jian-hua, Liu Dong-ning, et al. An Improved Recommendation Method Using Rating and Review Information[J]. 2017, 34(6): 27-31.
张巍, 黄健华, 刘冬宁, 滕少华, 刘子婷. 一种改进的结合评分和评论信息的推荐方法[J]. 广东工业大学学报, 2017, 34(6): 27-31. DOI： 10.12052/gdutxb.170014.

Zhang Wei, Huang Jian-hua, Liu Dong-ning, et al. An Improved Recommendation Method Using Rating and Review Information[J]. 2017, 34(6): 27-31. DOI： 10.12052/gdutxb.170014.

摘要

随着互联网技术的普及和现代电子商务的迅速发展

推荐系统得到广泛使用

但大多数推荐算法仍存在冷启动、可解释性差两大问题. 本文以结合评分和评论信息的HFT模型为基础

提出了一个改进的HFT模型

通过加入自由向量

捕获原HFT模型中未出现的评论信息

缓解了这两大问题

并进一步提高了模型的准确度. 最后

通过两个大型数据集的实验

结果表明本文的模型准确度优于HFT模型

可为有效利用评论信息资源提供参考.

Abstract

With the Internet technology and modern E-commerce becoming popular

the recommender system has been widely used

but two problems of most recommendation algorithms still remain

i.e. cold start and explanatory problem. Based on the HFT (Hidden Factors and Hidden Topics) model which combines the review and rating information

an improved HFT model is proposed. By adding the free vector in order to capture the review information not discussed in the HFT model

the two problems can be released and the model accuracy improved. At last

the two large datasets shows that the proposed model is better than the HFT model in accuracy

which can largely benefit the use of review information.

关键词

Keywords

references

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