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Title: Dynamic multi-concept user profile modelling in research paper recommender systems
Author: Al Alshaikh, Modhi
ISNI:       0000 0004 7427 1057
Awarding Body: University of Brighton
Current Institution: University of Brighton
Date of Award: 2018
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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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available