Dynamic multi-concept user profile modelling in research paper recommender systems

  • Modhi Al Alshaikh

    Student thesis: Doctoral Thesis


    The internet and the digital libraries are major sources of information for
    researchers, and there is an enormous growth of information on these sources. A large
    number of research papers are available which leads to the information overload
    problem and hence finding research papers that are related to users’ interests become
    difficult and time consuming. The field of recommender systems aims to solve the
    information overload problem by filtering information and providing users with
    relevant results. Although the current recommender systems provide recommendation
    services to users, different limitations and challenges have not been adequately
    addressed in the research paper domain. The work presented in this thesis contributes
    to the development of models and algorithms to the recommender systems in the
    research paper domain. The main aim of this thesis is to develop a dynamic multiconcept
    system that is able to recommend research papers of interest at appropriate
    times. The first contribution of this thesis is modelling dynamic user profiles that are
    able to adapt to the changes in multiple user interests and to be compatible with the
    requirements of advanced ontologies. The second contribution is analysing users’
    reading behaviour with research papers to develop novel short-term and long-term
    models that are able to adapt dynamically according to a user’s changing behaviour
    during his/her short and long term goals. These models can effectively learn different
    users’ reading behaviours implicitly without the need for any intervention from the
    user. The third contribution is predicting user’s future interests using a novel
    collaborative filtering approach without the need for the user ratings. All our proposed
    models are evaluated using offline evaluations with the BibSonomy dataset that
    contains actual users’ records. Our results show that our models outperform the
    baselines used for comparisons. Finally, we integrated our models to one unified
    dynamic hybrid system in order to provide recommendations which most closely
    represent the users’ research interests at particular times. The evaluation results
    indicate that the dynamic hybrid system that models and integrates multiple user
    interests and concepts can bring substantial benefits to a recommender system in the
    research paper domain.
    Date of Award2018
    Original languageEnglish
    Awarding Institution
    • University of Brighton
    SupervisorGulden Uchyigit (Supervisor)

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