一种基于社会化信任的改进的协同过滤推荐模型和方法
An Improved Collaborative Filtering Recommendation Model and Method Based on Social Trust
DOI: 10.12677/ECL.2018.82008, PDF,    国家自然科学基金支持
作者: 吴应良*：华南理工大学经济与贸易学院电子商务系，广东 广州；华南理工大学现代服务业研究院商务智能研究中心，广东 广州；黄开梅*：华南理工大学经济与贸易学院电子商务系，广东 广州；姚怀栋*：上海浦东发展银行海口分行，海南 海口
关键词: 社会化商务；社会关注矩阵；协同过滤推荐；预填充；个人可信度评分；互鉴可信度评分； Social Commerce； Social Attention Matrix； Collaborative Filtering Recommendation； Pre-Filling； Personal Reliability； Mutual Identify Reliability
摘要: 社会化商务环境中的信任关系深刻地影响着消费者的购买行为与决策，成为了支撑网络商务活动开展的重要因素。基于用户历史评价数据，协同过滤推荐算法通常面临着数据稀疏的问题，即评分数据过于稀疏导致推荐质量下降。为了解决这一问题，结合辅助数据成为一种必然的趋势。因此，随着社交媒体的发展，基于信任关系的社会化推荐算法被证明为一种有效的解决方法。然而，目前大部分算法直接利用社交网络的二值信任关系来提高推荐质量，没有考虑用户对每个好友信任强度的差异。为了提高社会化推荐算法的准确性，本文以社交数据为基础，计算用户个人可信度评分和互鉴可信度评分，并基于可信评分对社交关注矩阵进行可信量化，以及基于评分矩阵预填充的思想来缓解数据稀疏性问题。基于大众点评真实数据集的实验与分析结果表明，本文提出的新的协同过滤推荐模型与算法，进一步提高了推荐精度。
Abstract: Trust relationship in the social business environment has a profound impact on consumers' purchase behavior and decision-making, and has become an important factor to support the development of online business activities. Collaborative filtering recommendation algorithm based on user history evaluation data usually faces the problem of data sparseness; that is, the sparse rating data leads to the decline of recommendation quality. In order to solve this problem, the combination of auxiliary data has become an inevitable trend. Therefore, with the development of social media, trust-based social recommendation algorithm has been proved to be an effective solution. However, most of the current algorithms directly use the binary trust relationship of the social network to improve the recommendation quality, without considering the difference in the trust strength of the user for each friend. In order to improve the accuracy of social recommendation algorithm, this paper calculates personal reliability and mutual identify reliability based on social data, and quantifies the social attention matrix based on mutual identify reliability and alleviates the data sparsity problem based on the idea of score matrix pre-filling. The experiment and analysis results based on the real data set of public comments show that the new collaborative filtering recommendation model and algorithm proposed in this paper further improve the recommendation accuracy.
文章引用：吴应良, 黄开梅, 姚怀栋. 一种基于社会化信任的改进的协同过滤推荐模型和方法[J]. 电子商务评论, 2019, 8(2): 63-73. https://doi.org/10.12677/ECL.2018.82008

参考文献

[1] Ma, H., Yang, H.-X. and Michael, R. (2008) SoRec: Social Recommendation Using Probabilistic Matrix Factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, IEEE Press, New York, 931-941. [Google Scholar] [CrossRef] 
[2] 冯勇, 李军平, 徐红艳, 等. 基于社会网络分析的协同推荐方法改进[J]. 计算机应用, 2013, 33(3): 841-844.
[3] Massa, P. and Avesani, P. (2007) Trust-Aware Rec-ommender Systems. Proceedings of the 2007 ACM Conference on Recommender Systems, Minneapolis, 19-20 October 2007, 17-24. [Google Scholar] [CrossRef] 
[4] 俞琰, 邱广华. 用户兴趣变化感知的重启动随机游走推荐算法研究[J]. 现代图书情报技术, 2012(4): 48-53.
[5] Mohsen, J. and Martin, E. (2010) A Matrix Factor-ization Technique with Trust Propagation for Recommendation in Social Networks. Proceedings of the Fourth ACM Conference on Recommender Systems, Barcelona, 26-30 September 2010, 135-142.
[6] Yang, X.-W., Steck, H. and Liu, Y. (2012) Circle-Based Recommendation in Online Social Networks. Proceedings of the 18th ACM SIGKDD In-ternational Conference on Knowledge Discovery and Data Mining, Beijing, 12-16 August 2012, 155-165. [Google Scholar] [CrossRef] 
[7] Ma, H., Zhou, D.-Y. and Liu, C. (2011) Recommender Systems with Social Regularization. Proceedings of the 4th ACM International Conference on Web Search and Data Mining, Hong Kong, 9-12 February 2011, 287-297. [Google Scholar] [CrossRef] 
[8] 李春, 朱珍敏, 高晓芳. 基于邻居决策的协同过滤推荐算法[J]. 计算机工程, 2010, 36(13): 34-36.
[9] 罗辛, 欧阳元新, 熊璋. 通过相似度支持度优化基于K近邻的协同过滤算法[J]. 计算机学报, 2010, 33(8): 1437-1445.
[10] Massa, P. and Bhatacharjee, B. (2005) Using Trust in Recommender Systems: An Experimental Analysis. Proceedings of Conference on Trust Management, Paris, 23-26 May 2005, 221-235. [Google Scholar] [CrossRef] 
[11] Golbeck, J.A. (2005) Computing and Applying Trust in Web-Based Social Networks. Ph.D. Thesis.
[12] Zarghami, A., Fazeli, S., Dokoohaki, N., et al. (2009) Social Trust-Aware Recommendation System: A T-Index Approach. Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technologies, Milan, 15-18 September 2009, 85-90. [Google Scholar] [CrossRef] 
[13] Jamali, M. and Ester, M. (2009) Trustwalker: A Random Walk Model for Combining Trust-Based and Item-Based Recommendation. Proceedings of the 15th ACM SIGKDD Interna-tional Conference on Knowledge Discovery and Data Mining, Paris, 28 June-1July 2009, 397-406. [Google Scholar] [CrossRef] 
[14] Chen, X.C., Liu, R.J. and Chang, H.Y. (2010) Research of Col-laborative Filtering Recommendation Algorithm Based on Trust Propagation Model. Computer Application and System Modeling, Taiyuan, 22-24 October 2010, Vol. 4,177-183.
[15] Bedi, P. and Sharma, R. (2012) Trust Based Recom-mender System Using Ant Colony for Trust Computation. Expert Systems with Applications, 39, 1183-1190. [Google Scholar] [CrossRef] 
[16] 唐文, 陈钟. 基于模糊集合理论的主观信任管理模型研究[J]. 软件学报, 2003(8): 1401-1408.
[17] 陈婷, 朱青, 周梦溪, 等. 社交网络环境下基于信任的推荐算法[J]. 软件学报, 2017, 28(3): 721-731.
[18] 潘一腾, 何发智, 于海平. 一种基于信任关系隐含相似度的社会化推荐算法[J]. 计算机学报, 2018, 41(1): 65-81.
[19] 王瑞琴, 蒋云良, 李一啸, 等. 一种基于多元社交信任的协同过滤推荐算法[J]. 计算机研究与发展, 2016, 53(6): 1389-1399.
[20] 王占, 林岩. 基于信任与用户兴趣变化的协同过滤方法研究[J]. 情报学报, 2017, 36(2): 197-205.
[21] 吴应良, 姚怀栋, 李成安. 一种引入间接信任关系的改进协同过滤推荐算法[J]. 现代图书情报技术, 2015(9): 38-45.
[22] 杜巍, 高长元. 基于个性化情景的移动商务信任推荐模型研究[J]. 情报科学, 2017, 35(10): 23-29.
[23] 陈梅梅, 薛康杰. 基于标签簇多构面信任关系的个性化推荐算法研究[J]. 数据分析与知识发现, 2017, 1(5): 94-101.
[24] 薛福亮, 刘君玲. 基于用户间信任关系改进的协同过滤推荐方法[J]. 数据分析与知识发现, 2017, 1(7): 90-99.
[25] 闫光辉, 刘晓飞, 王梦阳. 基于链接的微博用户可信度研究[J]. 计算机应用研究, 2015, 32(10): 2910-2913+2917.

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