Browse > Article
http://dx.doi.org/10.3745/KIPSTB.2006.13B.6.625

Nearest-Neighbor Collaborative Filtering Using Dimensionality Reduction by Non-negative Matrix Factorization  

Ko, Su-Jeong (인덕대학 컴퓨터소프트웨어과)
Publication Information
The KIPS Transactions:PartB / v.13B, no.6, 2006 , pp. 625-632 More about this Journal
Abstract
Collaborative filtering is a technology that aims at teaming predictive models of user preferences. Collaborative filtering systems have succeeded in Ecommerce market but they have shortcomings of high dimensionality and sparsity. In this paper we propose the nearest neighbor collaborative filtering method using non-negative matrix factorization(NNMF). We replace the missing values in the user-item matrix by using the user variance coefficient method as preprocessing for matrix decomposition and apply non-negative factorization to the matrix. The positive decomposition method using the non-negative decomposition represents users as semantic vectors and classifies the users into groups based on semantic relations. We compute the similarity between users by using vector similarity and selects the nearest neighbors based on the similarity. We predict the missing values of items that didn't rate by a new user based on the values that the nearest neighbors rated items.
Keywords
Nearest-Neighbor Collaborative Filtering; Non-Negative Matrix Factorization; Dimensionality Reduction; User Variance Coefficient;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 김윤희, 'Non-negative Matrix Factorization을 이용한 텍스트 문서 및 마이크로어레이 데이터 분석', 서울대학교 학사 학위논문, 2003
2 김혜재, 손기락, 'K-최근접 이웃 추천 엔진에서의 벡터 유사도 사용에 대한 실험적 분석', 정보과학회 춘계 학술발표논문집(B), 제28권, 제1호, 2001   과학기술학회마을
3 B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, 'Application of Dimensionality Reduction in Recommender System A Case Study', Proceddings of ACM WebKDD, 2000
4 Rita Sharma and David Poole, 'Symmetric Collaborative Filtering Using the Noisy Sensor Model,' Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001
5 Simon Shepherd, Non-Negative Matrix Factorization, http://www.simonshepherd.supanet.com/nnmf.htm, 2004
6 Wei Xu , Xin Liu , and Yihong Gong, 'Document clustering based on non-negative matrix factorization,' Proceedings of the 26th annual international ACM SIGIR conference on Research and development in information retrieval, July 28-August 01, 2003   DOI
7 고수정, '협력적 여과 시스템에서 사용자 변동 계수를 이용한 기본 평가값 예측', 정보과학회논문지, 제32권, 11월, 2005   과학기술학회마을
8 MovieLens collaborative filtering data set, Http://www.cs.umn.edu/Research/GroupLens/index.html, GROUPLENS RESEARCH PROJECT, 2000
9 M. H. Pryor, 'The effects of singular value decomposition on collaborative filtering,' Technical Report PCS-TR98338, Compute Science Department, Dartmouth College, 1998
10 V. Rijsbergen and C. Joost, Information Retrieval, Butterworths, London-second edition, 1979
11 G. Salton and M. J. McGill, Introduction to Modern Irformation Retrieval, McGraw-Hill, 1983
12 B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, 'Application of Dimensionality Reduction in Recommender System A Case Study,' Proceddings of ACM WebKDD, 2000
13 B. Sarwar, J. Konstan, Al Borchers, J. Herlocker, B. Miller, and J. Riedl, 'Using Filtering' Agents to Improve Prediction Quality In the GroupLens Research Collaborative Filtering System,' Proceedings of the 1998 Conference on Computer Supported Cooperative Work, 1998   DOI
14 S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harslunan, 'Indexing by latent semantic analysis,' Journal of the American Society for Information Science, Vol. 41, No.6, 1990   DOI
15 J. Delgado and N. Ishii, 'Formal Models for Learning of User Preferences, a Preliminary Report,' In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI-99), 1999
16 L. Grossi, G. Gozzi, and P. Ganugi, 'Distribution Analysis of Items and Ratios in Companies? Accounts using a new iterative procedure,' Proceedings of compstat2002, 2002
17 Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John T. Riedl, 'Evaluating collaborative filtering recommender systems,' ACM Transactions on Information Systems (TOIS) archive, Vol. 22, No.1, 2004   DOI   ScienceOn
18 Lee, D. and Seung, H., 'Algorithms for non-negative matrix factorization,' Advances in Neural Information Processing Systems, pp. 556-562, 2001
19 Liu, W. and Yi, J., 'Existing and New algorithms for nonnegative matrix factorization', Tech. rep., Department of Computer Sciences, University of Texas at Austin, 2003
20 John. S. Breese, D. Heckerman, and C. Kadie, 'Empirical Analysis of Predictive Algoritluns for Collaborative Filtering,' Proceedings of the Conference on Uncertainty in Artificial Intelligence, Madison, WI, 1998
21 Giuseppe Carenini, Rita Sharma, 'Exploring More Realistic Evaluation Measures for Collaborative Filtering,' Proceeding of AAAl 2004, 2004
22 Sonny Han Seng Chee, Jiawei Han, and Ke Wang, 'RecTree: An Efficient Collaborative Filtering Method,' Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery, September, 2001