Clustering recommendation algorithm based on user interest and social trust

doi:10.11772/j.issn.1001-9081.2016.05.1273

Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (5): 1273-1278.DOI: 10.11772/j.issn.1001-9081.2016.05.1273

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Clustering recommendation algorithm based on user interest and social trust

XIAO Xiaoli^1,2, QIAN Yali^1,2, LI Danjiang^1,2, TAN Liubin³

1. Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation(Changsha University of Science and Technology), Changsha Hunan 410114 China;
2. College of Computer and Communication Engineering, Changsha University of Sciences and Technology, Changsha Hunan 410114, China;
3. College of New Media Technology, Hunan Mass Media Vocational Technical College, Changsha Hunan 410007, China

Received:2015-09-29 Revised:2015-11-04 Online:2016-05-09 Published:2016-05-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61303043), the Natural Science Foundation of Hunan Province (13JJ4052).

基于用户兴趣和社交信任的聚类推荐算法

肖晓丽^1,2, 钱娅丽^1,2, 李旦江^1,2, 谭柳斌³

1. 综合交通运输大数据智能处理湖南省重点实验室(长沙理工大学), 长沙 410114;
2. 长沙理工大学计算机与通信工程学院, 长沙 410114;
3. 湖南大众传媒技术学院新媒体技术学院, 长沙 410007

通讯作者: 钱娅丽
作者简介:肖晓丽(1965-),女,湖南邵阳人,教授,硕士,主要研究方向:数据挖掘、网络安全、移动通信、数据库;钱娅丽(1990-),女,湖南株洲人,硕士研究生,主要研究方向:数据挖掘、网络安全;李旦江(1992-),男,湖南浏阳人,硕士研究生,主要研究方向:数据挖掘、移动通信;谭柳斌(1975-),男,湖南长沙人,主要研究方向:网络安全、移动通信。
基金资助:
国家自然科学基金资助项目(61303043);湖南省自然科学基金资助项目(13JJ4052)。

Abstract

Abstract: Collaborative filtering algorithm is the most widely used algorithm in personalized recommendation system. Focusing on the problem of date sparseness and poor scalability, a new clustering recommendation algorithm based on user interest and social trust was proposed. Firstly, according to user rating information, the algorithm divided users into different categories by clustering technology, and set up a user neighbor set based on interest. In order to improve the accuracy of the calculation of interest similarity, the modified cosine formula was used to eliminate the difference of user scoring criteria. Then, the trust mechanism is introduced to measure implicit trust value among users by defining the direct trust calculation method and indirect trust calculation method, converted a social network to a trust network, and set up a user neighbor set based on trust. Finally, this algorithm combined the predictive value of two neighbor sets to generate recommendations for users by weighting method. The simulation experiment was carried out to test the performance on Douban dataset, found suitable value of α and k. Compared with collaborative filtering algorithm based on users and recommendation algorithm based on trust, the Mean Absolute Error (MAE) decreased by 6.7%, precision, recall and F1 increased by 25%,40% and 37%. The proposed algorithm can effectively improve the quality of recommendation system.

Key words: collaborative filtering, date sparseness, social trust, clustering technique, trust network

摘要： 个性化推荐系统中使用最广泛的算法是协同过滤算法,针对该算法存在的数据稀疏和扩展性差问题,提出了一种基于用户兴趣和社交信任的聚类推荐算法。该算法首先基于聚类技术根据用户评分信息将具有相同兴趣的用户聚为一类,并建立基于用户兴趣相近的邻居集合。为了提高兴趣相似度计算的准确性,采用了修正余弦计算公式来消除评分标准的差异问题。然后,引入信任机制,通过定义直接信任、间接信任、传递路径和计算方法来度量社交网络用户之间隐含的信任值,将社交网络转换为信任网络,依据信任程度来创建基于社交信任的邻居集合。通过加权的方式将基于两种邻居集合的预测值融合起来为用户产生项目的推荐。在Douban数据集上进行仿真实验,确定了最优的协调因子值和分类数值,并与基于用户的协同过滤算法和基于信任的推荐算法进行对比,实验结果表明,所提算法的平均绝对误差(MAE)减少了6.7%,准确率(precision)、覆盖(recall)和F1值分别增加了25%、40%和37%,有效提高了推荐系统的推荐质量。

关键词: 协同过滤, 数据稀疏, 社交信任, 聚类技术, 信任网络

CLC Number:

TP391.1

XIAO Xiaoli, QIAN Yali, LI Danjiang, TAN Liubin. Clustering recommendation algorithm based on user interest and social trust[J]. Journal of Computer Applications, 2016, 36(5): 1273-1278.

肖晓丽, 钱娅丽, 李旦江, 谭柳斌. 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.

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Clustering recommendation algorithm based on user interest and social trust

基于用户兴趣和社交信任的聚类推荐算法

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