• Login
    View Item 
    •   Home
    • Theses and Dissertations
    • Dissertations
    • View Item
    •   Home
    • Theses and Dissertations
    • Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    A Study of Fairness and Information Heterogeneity in Recommendation Systems

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Copyright_And_Availability_Form (Basmah Altaf - 118465).pdf
    Size:
    472.8Kb
    Format:
    PDF
    Download
    Thumbnail
    Name:
    PhD_Thesis_BasmahAltaf_RecommendationSystems.pdf
    Size:
    3.070Mb
    Format:
    PDF
    Download
    Thumbnail
    Name:
    Final Approval Form (Basmah Altaf - 118465).pdf
    Size:
    250.4Kb
    Format:
    PDF
    Download
    Type
    Dissertation
    Authors
    Altaf, Basmah cc
    Advisors
    Zhang, Xiangliang cc
    Committee members
    Moshkov, Mikhail cc
    Shihada, Basem cc
    Yan, Rui
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-11-21
    Embargo End Date
    2020-11-21
    Permanent link to this record
    http://hdl.handle.net/10754/660257
    
    Metadata
    Show full item record
    Access Restrictions
    At the time of archiving, the student author of this dissertation opted to temporarily restrict access to it. The full text of this dissertation will become available to the public after the expiration of the embargo on 2020-11-21.
    Abstract
    Recommender systems are an integral and successful application of machine learning in e-commerce industry and in everyday lives of online users. Recommendation algorithms are used extensively for news, musics, books, point of interests, or travel recommendation as well as in many other domains. Although much focus has been paid on improving recommendation quality, however, some real-world aspects are not considered: How to ensure that top-n recommendations are fair and not biased due to any popularity boosting events, such as awards for movies or songs? How to recommend items to entities by explicitly considering information from heterogeneous sources. What is the best way to model sequential recommendation systems as heterogeneous context-aware design, and learning on-the- y from spatial, temporal and social contexts. Can we model attributes and heterogeneous relations in a heterogeneous information network? The goal of this thesis is to pave the way towards the next generation of realworld recommendation systems tackling fairness and information heterogeneity challenges to improve the user experience, while giving good recommendations. This thesis bridges techniques from recommendation and deep-learning techniques for representation learning by proposing novel techniques to address the above real-world problems. We focus on four directions: (1) model the e ect of popularity bias over time on the consumption of items, (2) model the heterogeneous information associated with sequential history of users and social links for sequential recommendation, (3) model the heterogeneous links and rich content of nodes in an academic heterogeneous information network, and (4) learn semantics using topic modeling for nodes based on their content and heterogeneous links in a heterogeneous information network
    Citation
    Altaf, B. (2019). A Study of Fairness and Information Heterogeneity in Recommendation Systems. KAUST Research Repository. https://doi.org/10.25781/KAUST-1FRN7
    DOI
    10.25781/KAUST-1FRN7
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-1FRN7
    Scopus Count
    Collections
    Dissertations; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.