메뉴 건너뛰기




Volumn , Issue , 2013, Pages 313-322

Improve collaborative filtering through bordered block diagonal form matrices

Author keywords

Block Diagonal Form; Collaborative Filtering; Community Detection; Graph Partitioning

Indexed keywords

BLOCK DIAGONAL; BORDERED BLOCK DIAGONAL FORM; COLLABORATIVE FILTERING ALGORITHMS; COMMUNITY DETECTION; GRAPH PARTITIONING; PREDICTION ACCURACY; REAL-WORLD DATASETS; RECOMMENDATION ALGORITHMS;

EID: 84883126990     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2484028.2484101     Document Type: Conference Paper
Times cited : (33)

References (40)
  • 1
    • 10244251766 scopus 로고    scopus 로고
    • Permuting sparse rectangular matrices into block-diagonal from
    • C. Aykanat, A. Pinar, and U. V. Catalyurek. Permuting Sparse Rectangular Matrices into Block-Diagonal From. SISC, 2004.
    • (2004) SISC
    • Aykanat, C.1    Pinar, A.2    Catalyurek, U.V.3
  • 2
    • 49749086487 scopus 로고    scopus 로고
    • Scalable collaborative filtering with jointly derived neighborhood interpolation weights
    • R. M. Bell and Y. Koren. Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights. Proc. ICDM, 2007.
    • (2007) Proc. ICDM
    • Bell, R.M.1    Koren, Y.2
  • 3
    • 84879770012 scopus 로고    scopus 로고
    • A nested dissection approach to sparse matrix partitioning for parallel computations
    • E. Boman and M. Wolf. A Nested Dissection approach to Sparse Matrix Partitioning for Parallel Computations. Proc. AMM, 2007.
    • (2007) Proc. AMM
    • Boman, E.1    Wolf, M.2
  • 4
    • 33645673972 scopus 로고    scopus 로고
    • Fast online SVD revisions for lightweight recommender systems
    • M. Brand. Fast online SVD revisions for lightweight recommender systems. Proc. SIAM SDM, 2003.
    • (2003) Proc. SIAM SDM
    • Brand, M.1
  • 5
    • 0000301477 scopus 로고
    • Finding good approximate vertex and edge partitions is NP-hard
    • T. N. Bui and C. Jones. Finding Good Approximate Vertex and Edge Partitions is NP-hard. Inform. Process. Lett., 1992.
    • (1992) Inform. Process. Lett.
    • Bui, T.N.1    Jones, C.2
  • 6
    • 84861732705 scopus 로고    scopus 로고
    • Dense subgraph extraction with application to community detection
    • J. Chen and Y. Saad. Dense Subgraph Extraction with Application to Community Detection. TKDE, 2012.
    • (2012) TKDE
    • Chen, J.1    Saad, Y.2
  • 9
    • 74049087026 scopus 로고    scopus 로고
    • Community detection in graphs
    • S. Fortunato. Community Detection in Graphs. Physics Reports, 486:75-174, 2010.
    • (2010) Physics Reports , vol.486 , pp. 75-174
    • Fortunato, S.1
  • 10
    • 84879774433 scopus 로고    scopus 로고
    • Large-scale matrix factorization with distributed stochastic gradient descent
    • R. Gemulla, E. Nijkamp, P. J. Haas, and Y. Sismanis. Large-scale Matrix Factorization with Distributed Stochastic Gradient Descent. KDD, 2011.
    • (2011) KDD
    • Gemulla, R.1    Nijkamp, E.2    Haas, P.J.3    Sismanis, Y.4
  • 11
    • 34548591807 scopus 로고    scopus 로고
    • A scalable collaborative filtering framework based on co-clustering
    • T. George and S. Merugu. A Scalable Collaborative Filtering Framework based on Co-clustering. Proc. ICDM, 2005.
    • (2005) Proc. ICDM
    • George, T.1    Merugu, S.2
  • 12
    • 84898004184 scopus 로고    scopus 로고
    • Collaborative filtering ensemble
    • M. Jahrer and A. Toscher. Collaborative Filtering Ensemble. KDDCUP, 2011.
    • (2011) KDDCUP
    • Jahrer, M.1    Toscher, A.2
  • 13
    • 0242718336 scopus 로고    scopus 로고
    • Meshes, and Computing Fill-Reducing Orderings of Sparse Matrices-Version 5.0. University of Minnesota
    • G. Karypis. Metis-A Software Package for Partitioning Unstructured Graphs, Meshes, and Computing Fill-Reducing Orderings of Sparse Matrices-Version 5.0. University of Minnesota, 2011.
    • (2011) Metis-A Software Package for Partitioning Unstructured Graphs
    • Karypis, G.1
  • 14
    • 0032131147 scopus 로고    scopus 로고
    • A fast and high quality multilevel scheme for partitioning irregular graphs
    • G. Karypis and V. Kumar. A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs. SISC, 1998.
    • (1998) SISC
    • Karypis, G.1    Kumar, V.2
  • 15
    • 84860414934 scopus 로고    scopus 로고
    • Genetic approaches for graph partitioning: A survey
    • J. Kim, I. Hwang, Y. H. Kim, and B. R. Moon. Genetic Approaches for Graph Partitioning: A Survey. Proc. CECCO, pages 473-480, 2011.
    • (2011) Proc. CECCO , pp. 473-480
    • Kim, J.1    Hwang, I.2    Kim, Y.H.3    Moon, B.R.4
  • 16
    • 85008044987 scopus 로고    scopus 로고
    • Matrix factorization techniques for recommender systems
    • Y. Koren, R. Bell, and C. Volinsky. Matrix Factorization Techniques for Recommender Systems. Computer, 42:30-37, 2009.
    • (2009) Computer , vol.42 , pp. 30-37
    • Koren, Y.1    Bell, R.2    Volinsky, C.3
  • 17
    • 84898964201 scopus 로고    scopus 로고
    • Algorithms for non-negative matrix factorization
    • D. D. Lee and H. S. Seung. Algorithms for Non-negative Matrix Factorization. NIPS, 2001.
    • (2001) NIPS
    • Lee, D.D.1    Seung, H.S.2
  • 18
    • 80052112678 scopus 로고    scopus 로고
    • CLR: A collaborative location recommendation framework based on co-clustering
    • K. W. Leung, D. L. Lee, and W. Lee. CLR: A Collaborative Location Recommendation Framework based on Co-Clustering. Proc. SIGIR, 2011.
    • (2011) Proc. SIGIR
    • Leung, K.W.1    Lee, D.L.2    Lee, W.3
  • 19
    • 77952773265 scopus 로고    scopus 로고
    • A NMF-based privacy-preserving recommendation algorithm
    • T. Li, C. Gao, and J. Du. A NMF-based Privacy-Preserving Recommendation Algorithm. Proc. ICISE, 2009.
    • (2009) Proc. ICISE
    • Li, T.1    Gao, C.2    Du, J.3
  • 20
    • 84860854462 scopus 로고    scopus 로고
    • Community detection in incomplete information networks
    • W. Lin, X. Kong, P. S. Yu, Q. Wu, Y. Jia, and C. Li. Community Detection in Incomplete Information Networks. Proc. WWW, pages 341-349, 2012.
    • (2012) Proc. WWW , pp. 341-349
    • Lin, W.1    Kong, X.2    Yu, P.S.3    Wu, Q.4    Jia, Y.5    Li, C.6
  • 21
    • 77954604765 scopus 로고    scopus 로고
    • Distributed nonnegative matrix factorization for web-scale dyadic data analysis on MapReduce
    • C. Liu, H. Yang, J. Fan, L. He, and Y. Wang. Distributed Nonnegative Matrix Factorization for Web-scale Dyadic Data Analysis on MapReduce. Proc. WWW, pages 681-690, 2010.
    • (2010) Proc. WWW , pp. 681-690
    • Liu, C.1    Yang, H.2    Fan, J.3    He, L.4    Wang, Y.5
  • 23
    • 42549102609 scopus 로고    scopus 로고
    • Exploring local community structures in large networks
    • F. Luo, J. Z. Wang, and E. Promislow. Exploring Local Community Structures in Large Networks. Proc. WI, 2006.
    • (2006) Proc. WI
    • Luo, F.1    Wang, J.Z.2    Promislow, E.3
  • 24
    • 84862657771 scopus 로고    scopus 로고
    • Recommender system for music CDs using a graph partitioning method
    • T. Nakahara and H. Morita. Recommender System for Music CDs Using a Graph Partitioning Method. Proc. KES, 2009.
    • (2009) Proc. KES
    • Nakahara, T.1    Morita, H.2
  • 25
    • 84860868767 scopus 로고    scopus 로고
    • New objective functions for social collaborative filtering
    • J. Noel, S. Sanner, K. Tran, P. Christen, and L. Xie. New Objective Functions for Social Collaborative Filtering. Proc. WWW, pages 859-868, 2012.
    • (2012) Proc. WWW , pp. 859-868
    • Noel, J.1    Sanner, S.2    Tran, K.3    Christen, P.4    Xie, L.5
  • 27
    • 12744262778 scopus 로고    scopus 로고
    • Application of matrix clustering to web log analysis and access prediction
    • S. Oyanagi, K. Kubota, and A. Nakase. Application of Matrix Clustering to Web Log Analysis and Access Prediction. Proc. WEBKDD, pages 13-21, 2001.
    • (2001) Proc. WEBKDD , pp. 13-21
    • Oyanagi, S.1    Kubota, K.2    Nakase, A.3
  • 28
  • 29
    • 85030174634 scopus 로고
    • GroupLens: An open architecture for collaborative filtering of netnews
    • P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. GroupLens: An Open Architecture for Collaborative Filtering of Netnews. CSCW, 1994.
    • (1994) CSCW
    • Resnick, P.1    Iacovou, N.2    Suchak, M.3    Bergstrom, P.4    Riedl, J.5
  • 30
    • 84883064004 scopus 로고    scopus 로고
    • Engineering multilevel graph partitioning algorithms
    • P. Sanders and C. Schulz. Engineering Multilevel Graph Partitioning Algorithms. ESA, 2011.
    • (2011) ESA
    • Sanders, P.1    Schulz, C.2
  • 31
    • 85052617391 scopus 로고    scopus 로고
    • Item-based collaborative filtering recommendation algorithms
    • B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-Based Collaborative Filtering Recommendation Algorithms. Proc. WWW, pages 285-295, 2001.
    • (2001) Proc. WWW , pp. 285-295
    • Sarwar, B.1    Karypis, G.2    Konstan, J.3    Riedl, J.4
  • 32
    • 70749101696 scopus 로고    scopus 로고
    • Incremental singular value decomposition algorithms for highly scalable recommender systems
    • B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems. Proc. ICCIT, 2002.
    • (2002) Proc. ICCIT
    • Sarwar, B.1    Karypis, G.2    Konstan, J.3    Riedl, J.4
  • 33
    • 3042788736 scopus 로고    scopus 로고
    • Application of dimensionality reduction in recommender systems - A case study
    • B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedl. Application of Dimensionality Reduction in Recommender Systems - a case study. WebKDD, 2000.
    • (2000) WebKDD
    • Sarwar, B.M.1    Karypis, G.2    Konstan, J.A.3    Riedl, J.T.4
  • 34
    • 84864650291 scopus 로고    scopus 로고
    • Clustered low rank approximation of graphs in information science applications
    • B. Savas and I. S. Dhillon. Clustered Low Rank Approximation of Graphs in Information Science Applications. Proc. SIAM SDM, pages 164-175, 2011.
    • (2011) Proc. SIAM SDM , pp. 164-175
    • Savas, B.1    Dhillon, I.S.2
  • 35
    • 84864650291 scopus 로고    scopus 로고
    • Clustered low rank approximation of graphs in information science applications
    • B. Savas and I. S. Dhillon. Clustered Low Rank Approximation of Graphs in Information Science Applications. Proc. SIAM SDM, pages 164-175, 2011.
    • (2011) Proc. SIAM SDM , pp. 164-175
    • Savas, B.1    Dhillon, I.S.2
  • 36
    • 79960845914 scopus 로고    scopus 로고
    • Community discovery using nonnegative matrix factorization
    • F. Wang, T. Li, X. Wang, S. Zhu, and C. Ding. Community Discovery using Nonnegative Matrix Factorization. Journal of DMKD, 22, 2011.
    • Journal of DMKD , vol.22 , pp. 2011
    • Wang, F.1    Li, T.2    Wang, X.3    Zhu, S.4    Ding, C.5
  • 38
    • 84870773314 scopus 로고    scopus 로고
    • Efficient multicore collaborative filtering
    • Y. Wu, Q. Yan, D. Bickson, et al Efficient Multicore Collaborative Filtering. KDDCUP, 2011.
    • (2011) KDDCUP
    • Wu, Y.1    Yan, Q.2    Bickson, D.3
  • 39
    • 84860870379 scopus 로고    scopus 로고
    • An exploration of improving collaborative recommender systems via user-item subgroups
    • B. Xu, J. Bu, and C. Chen. An Exploration of Improving Collaborative Recommender Systems via User-Item Subgroups. WWW, pages 21-30, 2012.
    • (2012) WWW , pp. 21-30
    • Xu, B.1    Bu, J.2    Chen, C.3
  • 40
    • 33745478821 scopus 로고    scopus 로고
    • Learning from incomplite ratings using non-negative matrix factorization
    • S. Zhang, W. Wang, J. Ford, and F. Makedon. Learning from Incomplite Ratings Using Non-negative Matrix Factorization. Proc. SIAM SDM, 2006.
    • (2006) Proc. SIAM SDM
    • Zhang, S.1    Wang, W.2    Ford, J.3    Makedon, F.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.