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现代图书情报技术  2015, Vol. 31 Issue (5): 34-41     https://doi.org/10.11925/infotech.1003-3513.2015.05.05
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
一种基于项目聚类的自主推荐多样性优化算法
姜书浩1, 潘旭华1, 薛福亮2
1 天津商业大学信息工程学院 天津 300134;
2 天津财经大学商学院 天津 300222
An Independent Recommendation Diversity Optimization Algorithm Based on Item Clustering
Jiang Shuhao1, Pan Xuhua1, Xue Fuliang2
1 Information Engineering College, Tianjin University of Commerce, Tianjin 300134, China;
2 Business School, Tianjin University of Finance and Economics, Tianjin 300222, China
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摘要 

[目的]通过聚类权重再分配算法优化推荐列表的多样性。[方法]提出一种提高推荐多样性的方法, 依据项目评分进行聚类, 参照阈值采用聚类权重再分配算法重新分配各聚类集的权重, 根据权重大小从各聚类集中筛选项目生成最终推荐列表。[结果]实验结果表明, 调整阈值由20缩小到1, 本文方法将三种算法在MovieLens数据集上生成的推荐列表的z-多样性值分别提高0.46、0.65和1.88, Book-Crossing数据集对应的z-多样性值分别提高0.38、0.49和0.76。[局限]仅适用于提高推荐列表的多样性, 对于总体多样性并没有涉及。[结论]有效提高推荐的多样性, 同时保证推荐的准确率和较低的时间复杂性。

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关键词 项目聚类推荐多样性优化算法用户满意度协同过滤    
Abstract

[Objective] To optimize the diversity of the recommendation list by clustering weight redistribution. [Methods] This paper presents an algorithm to improve the recommendation diversity. Clustering is based on item scores. Clustering weight redistribution algorithm is used to reassign each clustering weight, and final recommendation list is generated from each cluster according to the weight. [Results] Experimental results show that z-diversity values of the recommendation list generated is increased by 0.46, 0.65 and 1.88 respectively for three algorithms on MovieLens data set, and z-diversity values is increased by 0.38, 0.49 and 0.76 respectively on Book-Crossing data set, when threshold is reduced from 20 to 1. [Limitations] This algorithm only applies to improve the recommendation list and does not involve the aggregate diversity. [Conclusions] This algorithm effectively improves diversity, while ensuring accuracy and lower time complexity compared with bounded greedy algorithm.

Key wordsItem clustering    Recommendation diversity    Optimization algorithm    Customer satisfaction    Collaborative filtering
收稿日期: 2014-10-31      出版日期: 2015-06-11
:  TP301.6  
基金资助:

本文系教育部人文社会科学一般项目“电子商务环境下顾客购物偏好推荐及企业利润挖掘”(项目编号:13YJC630195)的研究成果之一。

通讯作者: 姜书浩,ORCID:0000-0002-7706-063X,E-mail:mr_jiang1980@163.com。     E-mail: mr_jiang1980@163.com
作者简介: 作者贡献声明: 姜书浩:提出研究思路,设计研究方案,起草论文;潘旭华,姜书浩:实验过程设计;薛福亮,姜书浩:数据采集和分析,算法的多样性分析由薛福亮完成,算法比较以及复杂度的运算由姜书浩完成;姜书浩,潘旭华:论文修改及最终版本修订。
引用本文:   
姜书浩, 潘旭华, 薛福亮. 一种基于项目聚类的自主推荐多样性优化算法[J]. 现代图书情报技术, 2015, 31(5): 34-41.
Jiang Shuhao, Pan Xuhua, Xue Fuliang. An Independent Recommendation Diversity Optimization Algorithm Based on Item Clustering. New Technology of Library and Information Service, 2015, 31(5): 34-41.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2015.05.05      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2015/V31/I5/34

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