›› 2012, Vol. 27 ›› Issue (6): 1252-1260.doi: 10.1007/s11390-012-1301-5

• Artificial Intelligence and Pattern Recognition • Previous Articles     Next Articles

JacUOD: A New Similarity Measurement for Collaborative Filtering

Hui-Feng Sun1 (孙慧峰), Student Member, CCF, ACM, Jun-Liang Chen1 (陈俊亮), Gang Yu1 (俞钢), Member, IEEE, Chuan-Chang Liu1 (刘传昌), Member, IEEE, Yong Peng1 (彭泳), Guang Chen2 (陈光), and Bo Cheng1 (程渤), Senior Member, CCF, Member, ACM   

  1. 1. State Key Lab of Network and Switching Technology, Beijing University of Posts and Telecommunications Beijing 100876, China;
    2. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications Beijing 100876, China
  • Received:2011-08-23 Revised:2012-01-19 Online:2012-11-05 Published:2012-11-05
  • Supported by:

    This work was supported by the National Basic Research 973 Program of China under Grant No. 2011CB302506, the National Natural Science Foundation of China under Grant Nos. 61001118, 61132001, 61003067, the National Major Science and Technology Project of New Generation Broadband Wireless Network of China under Grant No. 2010ZX03004-001, and the Fundamental Research Funds for the Central Universities of Beijing University of Posts and Telecommunications of China under Grant No. 2011RC0502.

Collaborative filtering (CF) has been widely applied to recommender systems, since it can assist users to discover their favorite items. Similarity measurement that measures the similarity between two users or items is critical to CF. However, traditional similarity measurement approaches for memory-based CF can be strongly improved. In this paper, we propose a novel similarity measurement, named Jaccard Uniform Operator Distance (JacUOD), to effectively measure the similarity. Our JacUOD approach aims at unifying similarity comparison for vectors in different multidimensional vector spaces. Compared with traditional similarity measurement approaches, JacUOD properly handles dimension-number difference for different vector spaces. We conduct experiments based on the well-known MovieLens datasets, and take user-based CF as an example to show the effectiveness of our approach. The experimental results show that our JacUOD approach achieves better prediction accuracy than traditional similarity measurement approaches.

CLC Number: 

  • null
[1] Balabanovic M, Shoham Y. Fab: Content-based collaborativerecommendation. Comm. ACM, 1997, 40(3): 66-72.

[2] Lang K. NewsWeeder: Learning to filter netnews. In Proc.the 12th Int. Conf. Machine Learning, Jul. 1995, pp.331-339.

[3] Mooney R J, Roy L. Content-based book recommending usinglearning for text categorization. In Proc. ACM SIGIR 1999Workshop Recommender Systems: Algorithms and Evalua-tion, Aug. 1999, pp.195-204.

[4] Pazzani M, Billsus D. Learning and revising user profiles:The identification of interesting web sites. Machine Learn-ing, 1997, 27(3): 313-331.

[5] Xue G R, Dai W Y, Yang Q, Yu Y. Topic-bridged PLSA forcross-domain text classification. In Proc. the 31st Conf. Re-search and Development in Information Retrieval, Jul. 2008,pp.627-634.

[6] Liu N N, Yang Q. EigenRank: A ranking-oriented approach tocollaborative filtering. In Proc. the 31st Conf. Research andDevelopment in Information Retrieval, Jul. 2008, pp.83-90.

[7] Breese J, Heckerman D, Kadie C. Empirical analysis of predic-tive algorithms for collaborative filtering. In Proc. the 14thInt. Conf. Uncertainty in Artificial Intelligence (UAI 1998),May 1998, pp.43-52.

[8] Liu N N, Zhao M, Yang Q. Probabilistic latent prefer-ence analysis for collaborative filtering. In Proc. the18th Int. Conf. Information and Knowledge Management(CIKM2009), Nov. 2009, pp.759-766.

[9] Xin X, King L, Deng H, Lyu M R. A social recommendationframework based on multi-scale continuous conditional ran-dom fields. In Proc. the 18th ACM Conf. Information andKnowledge Management, Nov. 2009, pp.1247-1256.

[10] Adomavicius G, Tuzhilin A. Toward the next generation ofrecommender systems: A survey of the state-of-the-art andpossible extensions. IEEE Trans. Knowledge and Data En-gineering, 2005, 17(6): 734-749.

[11] Herlocker J L, Konstan J A, Borchers A, Riedl J. An algo-rithmic framework for performing collaborative filtering. InProc. the 22nd Conf. Research and Development in Infor-mation Retrieval, Aug. 1999, pp.230-237.

[12] Jin R, Chai J Y, Si L. An automatic weighting scheme forcollaborative filtering. In Proc. the 27th Conf. Research andDevelopment in Information Retrieval, Jul. 2004, pp.337-344.

[13] Zhao Z, Shang M. User-based collaborative-filtering recom-mendation algorithms on Hadoop. In Proc. the 3rd Int.Conf. Knowledge Discovery and Data Mining, Jan. 2010,pp478-481.

[14] Deshpande M, Karypis G. Item-based top-N recommenda-tion algorithms. ACM Transactions on Information Systems,2004, 22(1): 143-177.

[15] Linden G, Smith B, York J. Amazon.com recommendations:Item-to-item collaborative filtering. IEEE Internet comput-ing, 2003, 7(1): 76-80.

[16] Sarwar B, Karypis G, Konstan J, Reidl J. Item-based collabo-rative filtering recommendation algorithms. In Proc. the 10thInt. World Wide Web Conference, May 2001, pp.285-295.

[17] Ma H, King I, Lyu M. Effective missing data prediction forcollaborative filtering. In Proc. the 30th Conf. Research andDevelopment in Information Retrieval, Jul. 2007, pp.39-46.

[18] Wang J, Vries De A, Reinders M. Unifying user-based anditem-based collaborative filtering approaches by similarity fu-sion. In Proc. the 29th Conf. Research and Development inInformation Retrieval, Aug. 2006, pp.501-508.

[19] Zheng Z, Ma H, Lyu M R, King I. WSRec: A collaborativefiltering based Web service recommender system. In Proc.the 7th Int. Conf. Web Services, Jul. 2009, pp.437-444.

[20] Zheng Z, Ma H, Lyu M R, King I. QoS-awareWeb service rec-ommendation by collaborative filtering. IEEE Transactionson Service Computing, 2011, 4(2): 140-152.

[21] Resnick P, Iacovou N, Sushak M, Bergstrom P, Riedl J. Grou-pLens: An open architecture for collaborative filtering of net-news. In Proc. the 1994 ACM Conf. Computer SupportedCooperative Work, Oct. 1994, pp.175-186.

[22] Shardanand U, Maes P. Social information filtering: Algo-rithms for automating "Word of Mouth". In Proc. HumanFactors in Computing Systems, May 1995, pp.210-217.

[23] Candillier L, Meyer F, Fessant F. Designing specific weightedsimilarity measures to improve collaborative filtering systems.In Proc. the 12th Industrial Conf. Data Mining, Jul. 2008,pp.242-255.

[24] Ahn J H. A new similarity measure for collaborative filteringto alleviate the new user cold-starting problem. InformationSciences, 2008, 178(1): 37-51.

[25] Zeng C, Xing C X, Zhou L Z, Zheng X H. Similarity measureand instance selection for collaborative filtering. Int. Journalof Electronic Commerce, 2004, 8(4): 115-129.

[26] Fouss F, Pirotte A, Renders J M, Saerens M. Random-walkcomputation of similarities between nodes of a graph withapplication to collaborative recommendation. IEEE Trans.Knowledge and Data Engineering, 2007, 19(3): 355-369.

[27] Symeonidis P, Nanopoulos A, Papadopoulos A N, Manolopou-los Y. Collaborative filtering: Fallacies and insights in mea-suring similarity. In Proc. the 10th PKDD Workshop on WebMining, Sept. 2006, pp.56-67.

[28] Shi Y, Larson M, Hanjalic A. Exploiting user similarity basedon rated-item pools for improved user-based collaborative fil-tering. In Proc. the 3rd ACM Conf. Recommender Systems,Oct. 2009, pp.125-132.

[29] Miller B, Albert I, Lam S, Konstan J, Riedl J. MovieLensunplugged: Experiences with an occasionally connected rec-ommender system. In Proc. the 8th International Conferenceon Intelligent User Interfaces, Jan. 2003, pp.263-266
[1] Ying-Jun Wu(吴英骏), Han Huang(黄翰), Member, CCF, ACM, IEEE, Zhi-Feng Hao(郝志峰), and Feng Chen(陈丰). Local Community Detection Using Link Similarity [J]. , 2012, 27(6): 1261-1268.
[2] Punam BediMember, ACM, Senior Member, IEEE, Anjali Thukral, Hema Banati, Abhishek Behl, and Varun Mendiratta. A Multi-Threaded Semantic Focused Crawler [J]. , 2012, 27(6): 1233-1242.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] Gao Qingshi; Zhang Xiang; Yang Shufan; Chen Shuqing;. Vector Computer 757[J]. , 1986, 1(3): 1 -14 .
[2] Han Jianchao; Shi Zhongzhi;. Formalizing Default Reasoning[J]. , 1990, 5(4): 374 -378 .
[3] Zhang Bo; Zhang Ling;. On Memory Capacity of the Probabilistic Logic Neuron Network[J]. , 1993, 8(3): 62 -66 .
[4] Luo Junzhou; Gu Guanqun;. CIMS Network Protocol and Its Net Models[J]. , 1997, 12(5): 476 -481 .
[5] WAN Huagen; JIN Xiaogang; BAO Hujun;. Direct 3D Painting with a Metaball-Based Paint brush[J]. , 2000, 15(1): 100 -104 .
[6] ZHANG Wensong; JIN Shiyao; WU Quanyuan;. LinuxDirector: A Connection Director for Scalable Internet Services[J]. , 2000, 15(6): 560 -571 .
[7] Zhong-Xuan Liu, Shi-Guo Lian, and Zhen Ren. Quaternion Diffusion for Color Image Filtering[J]. , 2006, 21(1): 126 -136 .
[8] Zhi-Hua Zhou. Multi-Instance Learning from Supervised View[J]. , 2006, 21(5): 800 -809 .
[9] Han-Bing Yan, Shi-Min Hu, and Ralph R Martin. 3D Morphing Using Strain Field Interpolation[J]. , 2007, 22(1): 147 -155 .
[10] Juan J. Cuadrado Gallego, Daniel Rodri guez, Miguel Angel Sicilia, Miguel Garre Rubio and Angel Garci a Crespo. Software Project Effort Estimation Based on Multiple Parametric Models Generated Through Data Clustering[J]. , 2007, 22(3): 371 -378 .

ISSN 1000-9000(Print)

         1860-4749(Online)
CN 11-2296/TP

Home
Editorial Board
Author Guidelines
Subscription
Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
Tel.:86-10-62610746
E-mail: jcst@ict.ac.cn
 
  Copyright ©2015 JCST, All Rights Reserved