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Artificial Intelligence-Driven Recommender Solutions for E-Commerce: A Multidisciplinary Approach for Enhancing Collaborative Filtering Quality.

Title:

Artificial Intelligence-Driven Recommender Solutions for E-Commerce: A Multidisciplinary Approach for Enhancing Collaborative Filtering Quality.

Alshareet, Osama M. M. (2024) Artificial Intelligence-Driven Recommender Solutions for E-Commerce: A Multidisciplinary Approach for Enhancing Collaborative Filtering Quality. PhD thesis, Concordia University.

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Abstract

AI-driven recommender systems are transforming E-commerce by taking on tasks traditionally handled by human staff, such as sales associates, inventory clerks, and others. They apply machine learning methods to replace human decisions, enhance accuracy and scalability, and improve customer experiences. 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 its multidisciplinary approach, integrating principles from systems engineering, software engineering, and TRIZ 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 facilitates the development of innovative solutions and AI tools to eliminate technical contradictions and enhance the performance of CFRSs.

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 CFRSs.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (PhD)
Authors:Alshareet, Osama M. M.
Institution:Concordia University
Degree Name:Ph. D.
Program:Information and Systems Engineering
Date:6 September 2024
Thesis Supervisor(s):Awasthi, Anjali
Keywords:Multidisciplinary approaches, collaborative filtering, AI-driven, e-commerce, recommender systems, diversity, novelty, fairness, long-tail items, cold start problem, TRIZ methodology, systems engineering, ISO/IEC 25010:2011 standards, machine learning, graph neural networks, evaluation overfitting.
ID Code:994890
Deposited By: Osama M. M. Alshareet
Deposited On:17 Jun 2025 14:00
Last Modified:17 Jun 2025 14:00

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