• Login
    View Item 
    •   DBS eSource Home
    • Masters Dissertations
    • Information & Communications Technology
    • View Item
    •   DBS eSource Home
    • Masters Dissertations
    • Information & Communications Technology
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Improvement of recall measure by deriving graph features for link prediction on machine learning algorithms

    View/Open
    msc_bildik_zb_2019.pdf (1.565Mb)
    Author
    Bildik, Zeliha Bilge
    Date
    2019
    Degree
    MSc in Data Analytics
    URI
    https://esource.dbs.ie/handle/10788/3963
    Publisher
    Dublin Business School
    Rights holder
    http://esource.dbs.ie/copyright
    Rights
    Items in eSource are protected by copyright. Previously published items are made available in accordance with the copyright policy of the publisher/copyright holder.
    Metadata
    Show full item record
    Abstract
    In recent years the volume of data has increased significantly creating new challenges and opportunities in dealing with the interconnected data. Although new technologies enable the processing of high volumes of information, it is still challenging to find the relationships within the data that realise the anticipated business value. Graph analysis is becoming increasingly important to find the insights from connected data and to leverage machine learning outcomes. This thesis presents graph analytics applied on the leading ACID compliant graph dbms Neo4j to derive the features to improve on the prediction of recommender algorithms. The research uses the Movielens dataset for benchmarking purposes. Python is used for building the data pipeline using embedded cypher and python machine learning libraries. The research demonstrates the effectiveness of link prediction as a method for derivation of the features for machine learning. The resultant improvements in recall are demonstrated.
    Collections
    • Information & Communications Technology

    Browse

    All of DBS eSourceCommunities & CollectionsBy Issue DateAuthorsSupervisorTitlesSubjectsThis CollectionBy Issue DateAuthorsSupervisorTitlesSubjects

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    DSpace software copyright © 2002-2022  DuraSpace
    Contact Us | Send Feedback
    DSpace Express is a service operated by 
    Atmire NV