|
|
- 2016
一种基于模糊信息熵的协同过滤推荐方法
|
Abstract:
摘要： 针对评分数据的稀疏性制约协同过滤推荐性能的情况,提出一种新的相似性度量方法。首先,定义了用户的模糊信息熵以反映用户评分偏好的不确定程度;其次,利用两两用户的模糊互信息衡量用户之间的相似程度;最后,同时考虑用户之间的模糊互信息和用户的模糊信息熵,并设计一种基于模糊信息熵的相似性度量方法以计算用户之间的相似性。在两个公开数据集上的试验结果表明:基于模糊信息熵的相似性度量方法能够降低数据稀疏性的影响,并能显著提高推荐系统的推荐性能。
Abstract: The performance of collaborative filtering was restricted by the sparsity of rating data. To solve this problem, a novel similarity measure based on fuzzy mutual information was proposed. First, the definition of user fuzzy information entropy was given to reflect the uncertainty degree of rating preference. Then, the fuzzy mutual information between users was introduced to measure the similarity degree between users. Finally, the fuzzy information entropy based on similarity measure method was designed to calculate the similarity between users by considering not only the fuzzy mutual information between users but also user fuzzy information entropy. Experimental results on two benchmark data sets showed that the fuzzy information entropy based similarity measure method could reduce the influence of the data sparsity, and the recommendation performance of systems had significant improvements
| [1] | ADOMAVICIUS G, TUZHILIN A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6):734-749. |
| [2] | ZHANG J, LIN Y, LIN M, et al. An effective collaborative filtering algorithm based on user preference clustering[J]. Applied Intelligence, 2016, 45(2):230-240. |
| [3] | 黄创光,印鉴,汪静,等.不确定近邻的协同过滤推荐算法[J].计算机学报,2010,33(8):1369-1377. HUANG Chuangguang, YIN Jian, WANG Jing, et al. Uncertain neighbors collaborative filtering recommendation algorithm[J]. Chinese Journal of Computers, 2010, 33(8):1369-1377. |
| [4] | SHI Yue, LARSON M, HANJALIC A. Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges[J]. ACM Computing Surveys, 2014, 47(1):3:1-3:45. |
| [5] | RESNICK P, IACOVOU N, SUCHAK M, et al. GroupLens: an open architecture for collaborative filtering of netnews[C] //Proc of the ACM Conference on Computer Supported Cooperative Work. New York, USA: ACM, 1994:175-186. |
| [6] | JAMALI M, ESTER M. TrustWalker: a random walk model for combining trust-based and item-based recommendation[C] //Proc of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2009: 397-406. |
| [7] | 张佳,林耀进,林梦雷,等.基于信息熵的协同过滤算法[J].山东大学学报(工学版),2016,46(2):43-50. ZHANG Jia, LIN Yaojin, LIN Menglei, et al. Entropy-based collaborative filtering algorithm[J]. Journal of Shandong University(Engineering Science), 2016, 46(2):43-50. |
| [8] | 吴湖,王永吉,王哲,等.两阶段联合聚类协同过滤算法[J].软件学报,2010,21(5):1042-1054. WU Hu, WANG Yongji, WANG Zhe, et al. Two-phase collaborative filtering algorithm based on co-clustering[J]. Journal of Software, 2010, 21(5):1042-1054. |
| [9] | BREESE J S, HECKERMAN D, KADIE C. Empirical analysis of predictive algorithms for collaborative filtering[C] //Proc of the 14th Conference on Uncertainty in Artificial Intelligence. San Francisco, USA: Morgan Kaufmann Publishers Inc, 1998:43-52. |
| [10] | 张佳,林耀进,林梦雷,等.基于目标用户近邻修正的协同过滤算法[J].模式识别与人工智能,2015,28(9):802-810. ZHANG Jia, LIN Yaojin, LIN Menglei, et al. Target users neighbors modification based collaborative filtering[J]. Pattern Recognition and Artificial Intelligence, 2015, 28(9): 802-810. |
| [11] | 杨兴耀,于炯,吐尔根·伊布拉音,等.融合奇异性和扩展过程的协同过滤模型[J].软件学报,2013,24(8):1868-1884. YANG Xingyao, YU Jiong, IBRAHIM Turgun, et al. Collaborative filtering model fusing singularity and diffusion process[J]. Journal of Software, 2013, 24(8): 1868-1884. |
| [12] | 林耀进,胡学钢,李慧宗.基于用户群体影响的协同过滤推荐算法[J].情报学报,2013,32(3):299-305. LIN Yaojin, HU Xuegang, LI Huizong. Collaborative filtering recommendation algorithm based on user group influence[J]. Journal of the China Society for Scientific and Technical Information, 2013, 32(3):299-305. |
| [13] | PAPAGELIS M, PLEXOUSAKIS D, KUTSURAS T. Alleviating the sparsity problem of collaborative filtering using trust inferences[C] //Proc of the 3rd International Conference on Trust Management. Berlin, Germany: Springer, 2005:224-239. |
| [14] | SARWAR B, KARPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms[C] //Proc of the 10th International Conference on World Wide Web. New York, USA: ACM, 2001: 285-295. |
| [15] | KALELI C. An entropy-based neighbor selection approach for collaborative filtering[J]. Knowledge-Based Systems, 2014, 56: 273-280. |
| [16] | BOBADILLA J, HERNANDO A, ORTEGA F, et al. Collaborative filtering based on significances[J]. Information Sciences, 2012, 185(1):1-17. |
