Electronic Thesis/Dissertation


Ant Colony Inspired Models for Trust-Based Recommendations Open Access

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The rapid growth of web-based social networks has led to many breakthroughs in the different services that can be provided by such networks. Some networks allow users to describe their relationships with other users beyond a basic connection. This dissertation focuses on trust in web-based social networks and how it can be utilized to enhance a user's experience within a recommender system. A definition of trust and its properties is presented followed by a detailed explanation of recommender systems, their application and techniques.The recommendation problem in recommender systems is considered to be an optimization problem and thus many optimization algorithms can be used in such systems. The focus in this dissertation is specific to one group of such algorithms, ant algorithms, and an overview of how they can be applied to optimization problems is presented. While studying ant algorithms, it was noticed that an unprecedented improvement could be presented in the form of a local pheromone initialization technique, which is added to the list of contributions of this dissertation. This dissertation presents a set of novel models that apply an ant-based algorithm to trust-based recommender systems. A total of five main models are presented where each model is designed with a specific purpose such as expanding the scope of the search in the solution space or dealing with cold start users, but ultimately all models aim to enhance the performance of the recommender system. In addition to the basic model, the enhanced models fall under two categories: localized models that increase the importance of trust within local computations, and dynamic models that increase the level of information sharing between the artificial agents in the system.The results of the conducted experiments are presented in this dissertation along with an analysis of the results highlighting the strengths of each model and the different situations in which each model is most suitable for application.The dissertation concludes by discussing the lessons learned from the work presented and the possible extensions that can be added to the presented findings, which can contribute to the fields of recommender systems and artificial intelligence.

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