메뉴 건너뛰기




Volumn 369, Issue , 2016, Pages 221-237

Collaborative filtering via sparse Markov random fields

Author keywords

Collaborative filtering; Dating recommendation; Markov random field; Movie recommendation; Recommender systems; Sparse graph learning

Indexed keywords

IMAGE SEGMENTATION; MARKOV PROCESSES; RECOMMENDER SYSTEMS; STRUCTURAL FRAMES;

EID: 84976613294     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2016.06.027     Document Type: Article
Times cited : (13)

References (35)
  • 1
    • 0003535936 scopus 로고
    • Categorical data analysis
    • Wiley-Interscience
    • [1] Agresti, A., Categorical data analysis. 1990, Wiley-Interscience.
    • (1990)
    • Agresti, A.1
  • 3
    • 0000913755 scopus 로고
    • Spatial interaction and the statistical analysis of lattice systems (with discussions)
    • [3] Besag, J., Spatial interaction and the statistical analysis of lattice systems (with discussions). J. R. Stat. Soc. Ser. B 36 (1974), 192–236.
    • (1974) J. R. Stat. Soc. Ser. B , vol.36 , pp. 192-236
    • Besag, J.1
  • 9
    • 84976605473 scopus 로고    scopus 로고
    • Markov fields on finite graphs and lattices, 1971, Unpublished manuscript.
    • [9] J.M. Hammersley, P. Clifford, Markov fields on finite graphs and lattices, 1971, Unpublished manuscript.
    • Hammersley, J.M.1    Clifford, P.2
  • 10
    • 0002123103 scopus 로고    scopus 로고
    • Dependency networks for inference, collaborative filtering, and data visualization
    • [10] Heckerman, D., Chickering, D.M., Meek, C., Rounthwaite, R., Kadie, C., Dependency networks for inference, collaborative filtering, and data visualization. J. Mach. Learn. Res. 1 (2001), 49–75.
    • (2001) J. Mach. Learn. Res. , vol.1 , pp. 49-75
    • Heckerman, D.1    Chickering, D.M.2    Meek, C.3    Rounthwaite, R.4    Kadie, C.5
  • 11
    • 0013344078 scopus 로고    scopus 로고
    • Training products of experts by minimizing contrastive divergence
    • [11] Hinton, G.E., Training products of experts by minimizing contrastive divergence. Neural Comput. 14 (2002), 1771–1800.
    • (2002) Neural Comput. , vol.14 , pp. 1771-1800
    • Hinton, G.E.1
  • 12
    • 3042742744 scopus 로고    scopus 로고
    • Latent semantic models for collaborative filtering
    • [12] Hofmann, T., Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22:1 (2004), 89–115.
    • (2004) ACM Trans. Inf. Syst. , vol.22 , Issue.1 , pp. 89-115
    • Hofmann, T.1
  • 13
    • 77955644905 scopus 로고    scopus 로고
    • Factor in the neighbors: Scalable and accurate collaborative filtering
    • [13] Koren, Y., Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Trans. Knowl. Discov. Data, 4(1), 2010, 1.
    • (2010) ACM Trans. Knowl. Discov. Data , vol.4 , Issue.1 , pp. 1
    • Koren, Y.1
  • 14
    • 0037252945 scopus 로고    scopus 로고
    • Amazon.com recommendations: Item-to-item collaborative filtering
    • [14] Linden, G., Smith, B., York, J., Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput. 7:1 (2003), 76–80.
    • (2003) IEEE Internet Comput. , vol.7 , Issue.1 , pp. 76-80
    • Linden, G.1    Smith, B.2    York, J.3
  • 17
    • 84927631075 scopus 로고    scopus 로고
    • Recommender system application developments: a survey
    • [17] Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G., Recommender system application developments: a survey. Decis. Supp. Syst. 74 (2015), 12–32.
    • (2015) Decis. Supp. Syst. , vol.74 , pp. 12-32
    • Lu, J.1    Wu, D.2    Mao, M.3    Wang, W.4    Zhang, G.5
  • 18
    • 84898970398 scopus 로고    scopus 로고
    • Modeling user rating profiles for collaborative filtering
    • MIT Press Cambridge, MA
    • [18] Marlin, B., Modeling user rating profiles for collaborative filtering. Advances in Neural Information Processing Systems, 16, 2004, MIT Press, Cambridge, MA, 627–634.
    • (2004) Advances in Neural Information Processing Systems , vol.16 , pp. 627-634
    • Marlin, B.1
  • 19
    • 84927774789 scopus 로고    scopus 로고
    • A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling
    • [19] Martinez-Cruz, C., Porcel, C., Bernabé-Moreno, J., Herrera-Viedma, E., A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling. Inf. Sci. 311 (2015), 102–118.
    • (2015) Inf. Sci. , vol.311 , pp. 102-118
    • Martinez-Cruz, C.1    Porcel, C.2    Bernabé-Moreno, J.3    Herrera-Viedma, E.4
  • 20
    • 0004059061 scopus 로고    scopus 로고
    • Advanced Mean Field Methods: Theory and Practice
    • Massachusetts Institute of Technology Press (MIT Press)
    • [20] Opper, M., Saad, D., Advanced Mean Field Methods: Theory and Practice. 2001, Massachusetts Institute of Technology Press (MIT Press).
    • (2001)
    • Opper, M.1    Saad, D.2
  • 21
    • 0003391330 scopus 로고
    • Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
    • Morgan Kaufmann San Francisco, CA
    • [21] Pearl, J., Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. 1988, Morgan Kaufmann, San Francisco, CA.
    • (1988)
    • Pearl, J.1
  • 22
    • 84957926983 scopus 로고    scopus 로고
    • N-dimensional Markov random field prior for cold-start recommendation
    • [22] Peng, F., Lu, J., Wang, Y., Xu, R.Y.-D., Ma, C., Yang, J., N-dimensional Markov random field prior for cold-start recommendation. Neurocomputing 191 (2016), 187–199.
    • (2016) Neurocomputing , vol.191 , pp. 187-199
    • Peng, F.1    Lu, J.2    Wang, Y.3    Xu, R.Y.-D.4    Ma, C.5    Yang, J.6
  • 23
    • 77951455815 scopus 로고    scopus 로고
    • High-dimensional ising model selection using l1-regularized logistic regression
    • [23] Ravikumar, P., Wainwright, M.J., Lafferty, J.D., High-dimensional ising model selection using l1-regularized logistic regression. Annals Stat. 38:3 (2010), 1287–1319.
    • (2010) Annals Stat. , vol.38 , Issue.3 , pp. 1287-1319
    • Ravikumar, P.1    Wainwright, M.J.2    Lafferty, J.D.3
  • 31
    • 84858792249 scopus 로고    scopus 로고
    • Preference networks: Probabilistic models for recommendation systems
    • P. Christen P.J. Kennedy J. Li I. Kolyshkina G.J. Williams ACS Gold Coast, Australia
    • [31] Truyen, T.T., Phung, D.Q., Venkatesh, S., Preference networks: Probabilistic models for recommendation systems. Christen, P., Kennedy, P.J., Li, J., Kolyshkina, I., Williams, G.J., (eds.) The 6th Australasian Data Mining Conference (AusDM) CRPIT, vol. 70, 2007, ACS, Gold Coast, Australia, 195–202.
    • (2007) The 6th Australasian Data Mining Conference (AusDM), CRPIT , vol.70 , pp. 195-202
    • Truyen, T.T.1    Phung, D.Q.2    Venkatesh, S.3
  • 35
    • 84893255966 scopus 로고    scopus 로고
    • Iterative similarity inference via message passing in factor graphs for collaborative filtering
    • IEEE
    • [35] Zou, J., Einolghozati, A., Ayday, E., Fekri, F., Iterative similarity inference via message passing in factor graphs for collaborative filtering. Information Theory Workshop (ITW), 2013 IEEE, 2013, IEEE, 1–5.
    • (2013) Information Theory Workshop (ITW), 2013 IEEE , pp. 1-5
    • Zou, J.1    Einolghozati, A.2    Ayday, E.3    Fekri, F.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.