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Recommending accommodations using machine learning provider in a cloud

Gašper Slapničar (2015) Recommending accommodations using machine learning provider in a cloud. EngD thesis.

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    Recommender systems are present almost everywhere on the web and can be the key to potentially improved business results. In this thesis we develop a production-ready recommender system for a website that offers eco-sustainable accommodations, that meet certain requirements (e.g. usage of solar energy, water filtering and reuse, waste recycling etc.). First we examine crucial big data technologies and some of the cloud-based machine learning platforms. We proceed to choose the best platform and use it to collect data and develop a recommender system, which returns predictions for a user, based on a matrix factorization algorithm (Alternating Least Squares, ALS). It also returns similar items based on Jaccard similarity and euclidian distance. We conclude with system evaluation by using Precision@k statistical measure. The evaluation results have shown 19% precision accuracy, which greatly exceeds the results of random recommendation that achieves 1% precision accuracy. We also propose a potential website implementation with the intention of improving business results.

    Item Type: Thesis (EngD thesis)
    Keywords: recommender system, parallel computing, machine learning, big data, matrix factorization
    Number of Pages: 60
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    izr. prof. dr. Zoran Bosnić3826Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1536565699)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 3076
    Date Deposited: 12 Sep 2015 13:08
    Last Modified: 15 Oct 2015 12:35
    URI: http://eprints.fri.uni-lj.si/id/eprint/3076

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