[1]黄裕洋,金远平.一种综合用户和项目因素的协同过滤推荐算法[J].东南大学学报(自然科学版),2010,40(5):917-921.[doi:10.3969/j.issn.1001-0505.2010.05.007]
 Huang Yuyang,Jin Yuanping.Collaborative filtering recommendation algorithm based on both user and item[J].Journal of Southeast University (Natural Science Edition),2010,40(5):917-921.[doi:10.3969/j.issn.1001-0505.2010.05.007]
点击复制

一种综合用户和项目因素的协同过滤推荐算法()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
40
期数:
2010年第5期
页码:
917-921
栏目:
计算机科学与工程
出版日期:
2010-09-20

文章信息/Info

Title:
Collaborative filtering recommendation algorithm based on both user and item
作者:
黄裕洋 金远平
东南大学计算机科学与工程学院,南京 210096
Author(s):
Huang Yuyang Jin Yuanping
School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
关键词:
协同过滤推荐 数据稀疏 相似性 评分预测
Keywords:
collaborative filtering recommendation data sparsity similarity rating prediction
分类号:
TP312
DOI:
10.3969/j.issn.1001-0505.2010.05.007
摘要:
针对用户评分数据极端稀疏情况下传统协同过滤推荐算法的不足,提出了一种综合用户和项目因素的最近邻协同过滤推荐(HCFR)算法.该算法首先以一种改进的相似性度量方法(ISIM)为基础,根据当前评分数据的稀疏情况,动态调节相似度的计算值,真实地反映彼此之间的相似性.然后,在产生推荐时综合考虑用户和项目的影响因素,分别计算目标用户和目标项目的最近邻集合.最后,根据评分数据的稀疏情况,自适应地调节目标用户和目标项目的最近邻对最终推荐结果的影响权重,并给出推荐结果.实验结果表明,与传统的只基于用户或基于项目的推荐算法相比,HCFR算法在用户评分数据极端稀疏情况下仍能显著地提高推荐系统的推荐质量.
Abstract:
To solve the shortcomings of the traditional collaborative filtering recommendation algorithms in the situation of extreme sparsity of user’s rating data, a hybrid collaborative filtering recommendation(HCFR)algorithm for the nearest neighbors based on users and items is proposed. First, on the basis of correlation similarity, this algorithm adopts an improved similarity measure method(ISIM)which can dynamically adjust the value of similarity according to the current state of sparse rating data and truly reflect the real situation. Then, in the process of generating recommendation results, both user factors and item factors are considered and the nearest neighbor sets of the active user and the active item are obtained. Finally, according to the sparsity of the user’s rating data, different self-adaptive influence weights of the neighbor sets of the active user and the active item are adjusted, and the final recommendation results are obtained. The experimental results show that compared with the traditional recommendation algorithms which are only based on user or item, the HCFR algorithm can effectively improve the recommendation quality even in the situation of extreme sparsity of user’s rating data.

参考文献/References:

[1] Deshpande M,Karypis G.Item-based top-N recommendation algorithms [J].ACM Trans Information System,2004,22(1):143-177.
[2] Sarwar B M,Karypis G,Konstan J,et al.Item-based collaborative filtering recommendation algorithms [C] //Proceedings of the 10th International World Wide Web Conference.Hong Kong,China,2001:285-295.
[3] Sun Xiaohua,Kong Fansheng,Ye Song.A comparison of several algorithms for collaborative filtering in startup stage [C] //Proceedings of the 2005 IEEE International Conference on Networking,Sensing and Controlling.Los Alamitos,CA,USA,2005:25-28.
[4] Sarwar B M,Karypis G.Application of dimensionality reduction in recommender systems:a case study [C] //Proceedings of ACM Web KDD Workshop on Web Mining for E-commerce.New York,USA,2000:114-121.
[5] Gong Songjie,Ye Hongwu.Joining user clustering and item based collaborative filtering in personalized recommendation services [C] //Proceedings of the 2009 International Conference on Industrial and Information Systems.Haikou,China,2009:149-151.
[6] Braak Paul,Abdullah Noraswaliza,Xu Yue.Improving the performance of collaborative filtering recommender systems through user profile clustering [C] //Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.Milan,Italy,2009:147-150.
[7] Xue G R,Lin C,Yang Q,et al.Scalable collaborative filtering using cluster-based smoothing [C] //Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.Salvador,Brazil,2005:114-121.
[8] Wang J,de Vries A P,Reinders M J.Unifying user-based and item-based collaborative filtering approaches by similarity fusion [C] //Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.Washington DC,USA,2006:501-508.
[9] 邓爱林,朱扬勇,施伯乐.基于项目评分预测的协同过滤推荐算法[J].软件学报,2003,14(9):1621-1628.
  Deng Ailin,Zhu Yangyong,Shi Bole.A collaborative filtering recommendation algorithm based on item rating prediction [J].Journal of Software,2003,14(9):1621-628.(in Chinese)
[10] 李聪,梁昌勇,马丽.基于领域最近邻的协同过滤推荐算法[J].计算机研究与发展,2008,45(9):1532-1538.
  Li Cong,Liang Changyong,Ma Li.A collaborative filtering recommendation algorithm based on domain nearest neighbor [J].Journal of Computer Research and Development,2008,45(9):1532-1538.(in Chinese)
[11] Ma Hao,King Irwin,Lyu Michael.Effective missing data prediction for collaborative filtering [C] //Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.Amsterdam,Netherlands,2007:39-46.
[12] 周军锋,汤显,郭景峰.一种优化的协同过滤推荐算法 [J].计算机研究与发展,2004,41(10):1842-1847.
  Zhou Junfeng,Tang Xian,Guo Jingfeng.An optimized collaborative filtering recommendation algorithm [J].Journal of Computer Research and Development,2004,41(10):1842-1847.(in Chinese)
[13] Tao Yufei,Yi Ke,Sheng Cheng,et al.Quality and efficiency in high dimensional nearest neighbor search [C] //Proceedings of the 35th SIGMOD International Conference on Management of Data.Rhode Island,USA,2009:563-576.
[14] McLaughlin M R,Herlocker J L.A collaborative filtering algorithm and evaluation metric that accurately model the user experience [C] //Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.Sheffield,UK,2004:329-336.

备注/Memo

备注/Memo:
作者简介: 黄裕洋(1986—),男,硕士生; 金远平(联系人),男,教授,ypjin@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(60973023).
引文格式: 黄裕洋,金远平.一种综合用户和项目因素的协同过滤推荐算法[J].东南大学学报:自然科学版,2010,40(5):917-921. [doi:10.3969/j.issn.1001-0505.2010.05.007]
更新日期/Last Update: 2010-09-20