Collaborative filtering by PSO-based MMMF

@article{SowminiDevi2014CollaborativeFB,
  title={Collaborative filtering by PSO-based MMMF},
  author={V. SowminiDevi and Venkateswara Rao Kagita and A. Pujari and V. Padmanabhan},
  journal={2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
  year={2014},
  pages={569-574}
}
  • V. SowminiDevi, Venkateswara Rao Kagita, +1 author V. Padmanabhan
  • Published 2014
  • Computer Science
  • 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
  • Matrix factorization (MF) techniques are one of the most succesful realisations of recommender systems based on collaborative filtering/prediction (CF). For instance, in a movie recommender system based on CF, the inputs to the system are user ratings on movies (items) the users have already seen. To predict user preferences on movies they have not yet watched one needs to understand the patterns in the partially observed rating matrix. It is possible to visualize this setting as a matrix… CONTINUE READING
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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 27 REFERENCES
    Matrix Factorization Techniques for Recommender Systems
    • 4,005
    • Open Access
    Large-scale matrix factorization with distributed stochastic gradient descent
    • 596
    • Open Access
    Bayesian Matrix Factorization with Side Information and Dirichlet Process Mixtures
    • 176
    • Open Access
    Maximum-Margin Matrix Factorization
    • 1,011
    • Open Access
    Collaborative Filtering via Ensembles of Matrix Factorizations
    • M. Wu
    • Computer Science
    • 2007
    • 100
    Evolving artificial neural networks
    • 2,128
    • Open Access
    Active Transfer Learning for Cross-System Recommendation
    • 72
    • Open Access
    A Cyclic Weighted Median Method for L1 Low-Rank Matrix Factorization with Missing Entries
    • 53
    • Open Access