融合可信度和时效标签的商品推荐算法

黄兰; 钱育蓉; 于炯; 郑洁; 马婉贞

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融合可信度和时效标签的商品推荐算法

更新时间：2026-09-04

- 融合可信度和时效标签的商品推荐算法
- Goods Recommendation Fusing Credibility and Timeliness-tag
- 微电子学与计算机 2017年34卷第6期页码：78-83
- 作者机构：
  
  1. 新疆大学软件学院,新疆,乌鲁木齐,830091
  2. 新疆大学信息科学与工程学院,新疆,乌鲁木齐,830046
- 作者简介：
- 基金信息：
  
  国家自然科学基金 61562086国家自然科学基金 61462079国家自然科学基金 61363083国家自然科学基金 61262088
- DOI：
  中图分类号：
- 纸质出版：2017
- 稿件说明：
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黄兰, 钱育蓉, 于炯, 郑洁, 马婉贞. 融合可信度和时效标签的商品推荐算法[J]. 微电子学与计算机, 2017, 34(6): 78-83.

HUANG Lan, QIAN Yu-rong, YU Jiong, et al. Goods Recommendation Fusing Credibility and Timeliness-tag[J]. 2017, 34(6): 78-83.
黄兰, 钱育蓉, 于炯, 郑洁, 马婉贞. 融合可信度和时效标签的商品推荐算法[J]. 微电子学与计算机, 2017, 34(6): 78-83. DOI：

HUANG Lan, QIAN Yu-rong, YU Jiong, et al. Goods Recommendation Fusing Credibility and Timeliness-tag[J]. 2017, 34(6): 78-83. DOI：

摘要

为了提高电子商务推荐系统的性能

提出了考虑可信度的基于时效性的用户-项目-属性-标签四部图模型

并针对该模型提出了一种新的推荐算法——融合可信度和时效标签的商品推荐算法.该模型算法改善新项目的冷启动问题; 提出了基于评分信息的、基于项目属性的和基于用户时效标签的3种个性化预测评分方法

将这3种评估方法融合

通过调节参数

和平衡因子

判断这3种评估方法的影响因素权值

从而提高推荐的准确度.结果表明该推荐算法既有较高的准确度

也有较高的新颖度

并且在某种程度上有效地处理了新项目推荐的冷启动问题.

Abstract

To solve improve the performance of e-commerce recommendation system

considering four graph model of credibility based on the user-projects-property-label of timeliness is proposed

And this paper proposes also a new recommendation algorithm-Goods Recommendation Fusing Credibility and Timeliness-tag(CRFCT) for the model.The model algorithm using reliability model judges user type to improve the cold start of the new project.3 kinds of personalized prediction scoring methods are proposed based on rating information and the attributes of the project and user timeliness method

incorporating the three evaluation methods

by adjusting the parameters of alpha

beta

and gamma balance factor to determine the weights of influence factors of the three evaluation methods

so as to improve the accuracy of recommendation.Experimenting show that has higher accuracy and also has higher novelty

and to some extent effective to deal with the cold start problem of the new project recommendation compared with the traditional recommendation algorithm.

关键词

Keywords

references

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Alper Bilge, Lhsan Gunes, Huseyin Polat. Robustness analysis of privacy-preserving model-based recommendation schemes[J]. Expert Systems with Applications, 2014, 41(8): 3671-3681. DOI: 3671

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肖晓丽. 基于用户兴趣和社交信任的聚类推荐算法[J]. 计算机应用, 2016, 36(5): 1273-1278. DOI: 1273

丁少衡. 基于用户属性和评分的协同过滤推荐算法[J]. 计算机工程与设计, 2015, 36(2): 487-491. DOI: 487

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