PhD Oral Exam - Osama Alshareet, Information Systems Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Recommender systems have been used widely to improve the personalization experience for E-commerce users. In this context, significant achievements have been witnessed in recent years in improving collaborative filtering-based recommender systems (CFRSs) through optimizing recall and normalized discounted cumulative gain (NDCG) metrics. Nonetheless, major issues remain that significantly limit the performance and generalization of these systems, such as diversity and novelty in recommendations, fairness, inclusion of long-tail items, the cold start problem, reproducibility, and evaluation overfitting. This study advocates the need for new approaches for addressing these problems comprehensively, moving beyond the traditional optimization metrics (recall and NDCG).

This work is novel in that principles from systems engineering, software engineering, and TRIZ are integrated into the development and optimization of CFRSs. Since systems engineering takes a holistic standpoint, it allows for the reasoning and optimization of user-item interactions. Meanwhile, Software engineering provides several systematic ways and techniques to analyze and improve the functional parts of CFRSs. The TRIZ methodology aids in achieving innovative solutions that eliminate technical contradictions to enhance CFRSs' performance.

To guide the optimization of CFRSs, the research also uses the ISO/IEC 25010:2011 standards to evaluate the CFRSs thoroughly. These standards evaluate the reliability, usability, performance efficiency, and privacy of the CFRSs against high-quality benchmarks. The evaluation of the results based on real-world data pertaining to e-commerce datasets demonstrates that the recommendation accuracy, diversity, and coverage were improved.

All in all, the current research improves CFRS technology by providing robust, innovative, and user-centric solutions. The proposed multidisciplinary approaches serve as a template for future research and development work. The findings achieved, peer-reviewed, and published in various publications have contributed to the discourse in academics along with practical implementation by creating high-quality CFRS applications.