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Comparison of latent models for recommender systems in machine learning

Demian Bucik (2019) Comparison of latent models for recommender systems in machine learning. EngD thesis.

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    The ubiquity of recommender systems in the digital ecosystem is thoroughly changing the way web services are used. In the ceaseless stream of information, users are relying on their recommendations to access interesting and useful content. The aim of this thesis is to present two approaches to collaborative filtering based recommender systems. Developing the models, we borrow certain concepts from the field of machine learning. All models are implemented in Python and evaluated on two datasets, one containing the numbers of plays and the other containing users' ratings of content. We propose a way to model continuous ratings with restricted Boltzmann machine and apply an addition to the matrix factorization model that makes it possible to model friendships between users. The results suggest that matrix factorization models work considerably better and that more time should be invested in their further development.

    Item Type: Thesis (EngD thesis)
    Keywords: collaborative filtering, machine learning, matrix factorization, restricted Boltzmann machine, maximum a posteriori estimation
    Number of Pages: 53
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    prof. dr. Zoran Bosnić3826Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1538172355)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 4400
    Date Deposited: 13 Mar 2019 17:00
    Last Modified: 26 Mar 2019 09:32
    URI: http://eprints.fri.uni-lj.si/id/eprint/4400

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