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Recommender system in a graph database

Jan Šmid (2018) Recommender system in a graph database. EngD thesis.

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    The thesis presents a recommender system, which is implemented using graph databases. The recommender system aims to predict the "rating" which the user would give to the element or predict which elements would be of interest to the user. There are several algorithms for recommendations. The more important approaches are: collaborative filtering, content-based filtering, and hybrid recommender systems. Graph databases are particularly suitable for such systems due to their data model. The most prominent representative is Neo4j. Based on the Neo4j system, we developed a recommender system to recommend movies (based on GroupLens data) and supported it with a web application. We used collaborative and content-based filtering. The results of the application were compared with the results of the Surprise tool. We found out that the values of MAE and RMSE are similar if we use the same algorithm.

    Item Type: Thesis (EngD thesis)
    Keywords: graph databases, recommender system, Neo4j.
    Number of Pages: 46
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    izr. prof. dr. Matjaž Kukar267Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1537947331)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 4250
    Date Deposited: 22 Sep 2018 13:22
    Last Modified: 03 Oct 2018 10:57
    URI: http://eprints.fri.uni-lj.si/id/eprint/4250

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