基于用户声誉的鲁棒协同推荐算法

张燕平; 张顺; 钱付兰; 张以文

doi:10.16383/j.aas.2015.c140073

基于用户声誉的鲁棒协同推荐算法

doi: 10.16383/j.aas.2015.c140073

张燕平^1,2,
张顺^1,2,
钱付兰^1,2, ,,
张以文^1,2

1.
安徽大学计算机科学与技术学院合肥 230601;
2.
安徽大学智能计算与信号处理教育部重点实验室合肥 230601

基金项目:

国家自然科学基金 (61175046), 安徽大学青年科学基金 (KJQN1116), 安徽省自然科学基金项目(1408085MF132),教育部人文社科青年基金(14YJC860020)资助

详细信息

作者简介:
张燕平安徽大学计算机科学与技术学院教授. 主要研究方向为商空间与智能计算.E-mail: zhangyp2@gmail.com

通讯作者:
钱付兰安徽大学计算机科学与技术学院博士研究生. 2005 年获得安徽大学硕士学位. 主要研究方向为社交网络与个性化推荐. E-mail: qianfulan@hotmail.com

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- 被引次数: 28
出版历程
- 收稿日期: 2014-01-28
- 修回日期: 2014-12-03
- 刊出日期: 2015-05-20

Robust Collaborative Recommendation Algorithm Based on User's Reputation

1.
School of Computer Science and Technology, Anhui University, Hefei 230601;
2.
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601

Funds:

Supported by National Natural Science Foundation of China (61175046), Youth Science Fund of Anhui University (KJQN1116), Natural Science Found of Anhui Province (1408085MF132), and Humanities and Social Science Youth Fund of Ministry of Education (14YJC860020)

摘要

摘要: 随着推荐系统在电子商务界的快速发展以及取得的巨大经济收益, 有目的性的托攻击是目前协同过滤系统面临的重大安全威胁, 研究一种可抵御攻击的鲁棒推荐技术已成为目前推荐系统领域的重要课题.本文利用历史记录得到用户声誉, 建立声誉推荐系统, 并结合协同过滤推荐领域内的隐语义模型, 提出基于用户声誉的隐语义模型鲁棒协同算法.本文提出的算法从人为攻击和自然噪声两个方面对系统的鲁棒性进行了改善.在真实的数据集 Movielens 1M 上的实验表明, 与现有的鲁棒性推荐算法相比, 这种算法具有形式简单、可解释性强、稳定的特点, 且在精度得到一定提升的情况下大大增强了系统抵御攻击的能力.
- 推荐系统 /
- 协同过滤 /
- 声誉 /
- 托攻击
Abstract: With the rapid development of recommender systems in e-commerce industry, such systems bring huge economic profits. As a consequence, shilling attacks pose a significant threat to the security of collaborative filtering recommender systems. Developing a kind of robust recommendation technology which can resist attacks has become an important issue in the field of the recommender system at present. In this paper, a reputation recommender system is built by user reputations which are obtained from the user historical records. Utilizing the latent factor model in the field of collaborative filtering recommendation, a novel robust collaborative recommendation algorithm based on user reputations is proposed. The algorithm improves the system's robustness from two aspects of shilling attack and natural noise. Empirical results on Movielens 1M dataset demonstrate that compared with the existing robust recommendation, this algorithm is very effective. Characterized by simplicity, interpretability and stability, the algorithm has strong ability to resist the system attack along with the accuracy getting a certain improvement.
- Recommender system /
- collaborative filtering /
- reputation /
- shilling attack

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参考文献(26)

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