《计算机应用研究》|Application Research of Computers

一种改进专家信任的协同过滤推荐算法

Improved expert trust collaborative filtering recommendation algorithm

免费全文下载 (已被下载 次)  
获取PDF全文
作者 王建芳,刘冉东,谷振鹏,刘永利
机构 河南理工大学 计算机科学与技术学院,河南 焦作 454000
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2018)02-0354-04
DOI 10.3969/j.issn.1001-3695.2018.02.008
摘要 针对传统基于用户的协同过滤算法较少考虑信任对象所处环境的实时变化,提出一种结合社交网络的专家信任推荐算法。为更好地量化对象之间的信任度,首先利用专家的评价可信度、活跃度、评价偏差度等量化因子计算得到专家的信任值;其次在评分形成的过程中与近邻算法相融合,明确用户与“专家”和“近邻”的偏好,当可选专家人数小于预先设定的阈值时,利用协调因子动态调整近邻算法与改进专家算法的权重,以便获得更加客观的项目评分。最终实验结果表明,在不同大小的MovieLens数据集上相比于传统的算法,提出的推荐算法在实时推荐预测准确度方面有显著提高。
关键词 专家算法;专家信任;信任指标;预测精度
基金项目 国家自然科学基金资助项目(61202286)
河南省高等学校青年骨干教师资助计划项目(2015GGJS-068)
河南省高等学校重点科研项目(15A520074)
本文URL http://www.arocmag.com/article/01-2018-02-008.html
英文标题 Improved expert trust collaborative filtering recommendation algorithm
作者英文名 Wang Jianfang, Liu Randong, Gu Zhenpeng, Liu Yongli
机构英文名 CollegeofComputerScience&Technology,HenanPolytechnicUniversity,JiaozuoHenan454000,China
英文摘要 To solve the tradition collaborative filtering recommendation based on the user object with little consideration of trust real-time change, this paper proposed an expert’s algorithm combined with social network trust to make good quantitative trust among objects.Firstly, the algorithm computed evaluation credibility, the active degree and the deviation degree to acquire the expert’s trust value.Then, it integrated the scores and neighbor algorithm, and determined the preference of the users and the experts, the users and the neighbor.When the numbers of the selected experts were less than the threshold, it used coordination factor to adjust dynamically the weight between the neighbor algorithm and the expert’s algorithm to get more objective score.Experimental results show that proposed algorithm achieves better result on the accuracy of recommendation.
英文关键词 expert algorithm; expert trust; trust indicator; prediction accuracy
参考文献 查看稿件参考文献
  [1] Piao Chunhui, Zhao Jing, Zheng Lijuan. Research on entropy-based collaborative filtering algorithm and personalized recommendation in ecommerce[J] . Service Oriented Computing & Applications, 2009, 3(2):147-157.
[2] Adomavicius G, Tuzhilin A. A survey of the state-of-the-art and possible extensions[J] . IEEE Trans on Knowledge and Data Engineering, 2005, 17(6):734-749.
[3] Keller J M, Gray M R, Givens J A. A fuzzy K-nearest neighbor algorithm[J] . IEEE Trans on Systems Man & Cybernetics, 1985, SMC-15(4):580-585.
[4] Amatriain X, Lathia N, Pojol J M, et al. The wisdom of the few:a collaborative filtering approach based on expert opinions from the Web[C] //Proc of International ACM SIGIR Conference on Research & Development in Information Retrieval. 2009:532-539.
[5] Kagita V R, Padmanabhan V, Pujari A K. Precedence mining in group recommender systems[M] . Berlin:Springer, 2013:701-707.
[6] Kardan A, Aziz M, Shahpasand M. Adaptive systems:a content analysis on technical side for e-learning environments[J] . Artificial Intelligence Review, 2015, 44(3):365-391.
[7] Hwang W S, Lee H J, Kim S W, et al. Efficient recommendation methods using category experts for a large dataset[J] . Information Fusion, 2015, 28(C):75-82.
[8] Zamanzad G F, Claesen J, Burzykowski T, et al. Comparison of the Mahalanobis distance and Pearson′sχ2 statistic as measures of similarity of isotope patterns[J] . Journal of the American Society for Mass Spectrometry, 2014, 25(2):293-296.
[9] Cho J, Kwon K, Park Y. Collaborative filtering using dual information sources[J] . IEEE Intelligent Systems, 2007, 22(3):30-38.
[10] Hwang W S, Lee H J, Kim S W, et al. Efficient recommendation methods using category experts for a large dataset[J] . Information Fusion, 2015, 28(C):75-82.
收稿日期 2016/10/14
修回日期 2016/11/29
页码 354-357,385
中图分类号 TP301.6
文献标志码 A