An Independent Recommendation Diversity Optimization Algorithm Based on Item Clustering
Jiang Shuhao1, Pan Xuhua1, Xue Fuliang2
1 Information Engineering College, Tianjin University of Commerce, Tianjin 300134, China;
2 Business School, Tianjin University of Finance and Economics, Tianjin 300222, China
[Objective] To optimize the diversity of the recommendation list by clustering weight redistribution. [Methods] This paper presents an algorithm to improve the recommendation diversity. Clustering is based on item scores. Clustering weight redistribution algorithm is used to reassign each clustering weight, and final recommendation list is generated from each cluster according to the weight. [Results] Experimental results show that z-diversity values of the recommendation list generated is increased by 0.46, 0.65 and 1.88 respectively for three algorithms on MovieLens data set, and z-diversity values is increased by 0.38, 0.49 and 0.76 respectively on Book-Crossing data set, when threshold is reduced from 20 to 1. [Limitations] This algorithm only applies to improve the recommendation list and does not involve the aggregate diversity. [Conclusions] This algorithm effectively improves diversity, while ensuring accuracy and lower time complexity compared with bounded greedy algorithm.
姜书浩, 潘旭华, 薛福亮. 一种基于项目聚类的自主推荐多样性优化算法[J]. 现代图书情报技术, 2015, 31(5): 34-41.
Jiang Shuhao, Pan Xuhua, Xue Fuliang. An Independent Recommendation Diversity Optimization Algorithm Based on Item Clustering. New Technology of Library and Information Service, 2015, 31(5): 34-41.
[1] Konstan J A, Miller B N, Maltz D, et al. Grouplens: Applying Collaborative Filtering to Usenet News [J]. Communications of the ACM, 1997, 40(3): 77-87.
[2] 陈雅茜. 音乐推荐系统及相关技术研究[J]. 计算机工程与应用, 2012, 48(18): 9-16. (Chen Yaxi. Research on Music Recommender Systems and Relevant Technologies [J]. Computer Engineering and Applications, 2012, 48(18): 9-16.)
[3] Golbeck J. Generating Predictive Movie Recommendations from Trust in Social Networks [C]. In: Proceedings of the 4th International Conference on Trust Management, Pisa, Italy. Springer, 2006: 93-104.
[4] Shih D H, Yen D C, Lin H C, et al. An Implementation and Evaluation of Recommender Systems for Traveling Abroad [J]. Expert Systems with Applications, 2011, 38(12): 15344-15355.
[5] Rosaci D, Sarnè G M L. A Multi-agent Recommender System for Supporting Device Adaptivity in E-commerce[C]. In: Proceedings of the 2nd International Symposium on Intelligent Distributed Computer, Catania, Italy. Springer, 2008: 293-298.
[6] 陶剑文, 姚奇富. 基于Web使用挖掘的个性化学习推荐系统 [J]. 计算机应用, 2007, 27(7): 1809-1812. (Tao Jianwen, Yao Qifu. Recommendation System Based on Web Usage Mining for Personalized E-learning [J]. Journal of Computer Applications, 2007, 27(7): 1809-1812.)
[7] Castells P, Wang J, Lara R, et al. Workshop on Novelty and Diversity in Recommender Systems-DiveRS 2011 [C]. In: Proceedings of the 5th ACM Conference on Recommender Systems, Chicago, IL, USA. 2011: 393-394.
[8] Hurley N, Zhang M. Novelty and Diversity in Top-N Recommendation-Analysis and Evaluation [J]. ACM Transactions on Internet Technology, 2011, 10(4): Article No.14.
[9] Herlocker J L, Konstan J A, Terveen L G, et al. Evaluating Collaborative Filtering Recommender Systems [J]. ACM Transactions on Information Systems, 2004, 22(1): 5-53.
[10] Zhang M, Hurley N. Avoiding Monotony: Improving the Diversity of Recommendation Lists [C]. In: Proceedings of the 2008 ACM Conference on Recommender Systems. 2008: 123-130.
[11] Smyth B, McClave P. Similarity vs. Diversity [C]. In: Proceedings of the 4th International Conference on Case-Based Reasoning, Vancouver, BC, Canada. 2011: 347-361.
[12] 刘慧婷, 岳可诚. 可提高多样性的基于推荐期望的Top-N推荐方法[J]. 计算机科学, 2014, 41(7): 270-274. (Liu Huiting, Yue Kecheng. Expection-based Top-N Recommenda-tion Approach for Improving Recommendations Diversity [J]. Computer Science, 2014, 41(7): 270-274.)
[13] Ziegler C N, McNee S M, Konstan J A, et al. Improving Recommendation Lists Through Topic Diversification [C]. In: Proceedings of the 14th International Conference on World Wide Web. 2005: 22-32.
[14] 李颖, 李永丽, 蔡观洋. 基于双重阈值近邻查找的协同过滤算法[J]. 吉林大学学报: 信息科学版, 2013, 31(6): 647-653. (Li Ying, Li Yongli, Cai Guanyang. Daul-Threshold Neighbors Finding Method for Neighborhood-Based Collaborative Filtering [J]. Journal of Jilin University: Information Science Edition, 2013, 31(6): 647-653.)
[15] Boim R, Milo T, Novgorodov S. Diversification and Refinement in Collaborative Filtering Recommender [C]. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. ACM, 2011: 739-744.
[16] 姜书浩, 薛福亮.一种利用协同过滤预测和模糊相似性改进的基于内容的推荐方法[J]. 现代图书情报技术, 2014(2): 41-47. (Jiang Shuhao, Xue Fuliang. An Improved Content-based Recommendation Method Through Collaborative Predictions and Fuzzy Similarity Measures [J]. New Technology of Library and Information Service, 2014(2): 41-47.)
[17] 朱郁筱, 吕琳媛. 推荐系统评价指标综述[J]. 电子科技大学学报, 2012, 41(2): 163-175. (Zhu Yuxiao, Lv Linyuan. Evaluation Metrics for Recommender Systems [J]. Journal of University of Electronic Science and Technology of China, 2012, 41(2): 163-175.)
[18] 孙吉贵, 刘杰, 赵连宇. 聚类算法研究[J] 软件学报, 2008, 19(1): 48-61. (Sun Jigui, Liu Jie, Zhao Lianyu. Clustering Algorithm Research [J]. Journal of Software, 2008, 19(1): 48-61.)
[19] Linden G, Smith B, York J. Amazon.com Recommendations: Item-to-Item Collaborative Filtering [J]. IEEE Internet Computing, 2003, 7(1): 76-80.
[20] 徐翔, 王煦法. 基于SVD的协同过滤算法的欺诈攻击行为分析[J]. 计算机工程与应用, 2009, 45(20): 92-95. (Xu Xiang, Wang Xufa. Analysis of Shilling Attacks on SVD-based Collaborative Filtering Algorithm [J]. Computer Engineering and Applications, 2009, 45(20): 92-95.)
[21] Tan P N, Steinbach M, Kumar V. Introduction to Data Mining [M]. Boston: Addison-Wesley. 2005: 178.
[22] 安维, 刘启华, 张李义. 个性化推荐系统的多样性研究进展[J]. 图书情报工作, 2013, 57(20): 127-135. (An Wei, Liu Qihua, Zhang Liyi. Review on Diversity in Personalized Recommender Systems [J]. Library and Information Service, 2013, 57(20): 127-135.)