Introduction

In the realm of higher education, theses and dissertations represent the culmination of a student's academic journey. These scholarly works reflect not only the student's expertise but also contribute to the body of knowledge within a specific discipline. As the volume of academic research continues to grow across universities and institutions, identifying relevant, high-quality theses or dissertations has become increasingly challenging for students, researchers, and faculty members alike. This growing repository of academic literature calls for intelligent tools that can streamline the research process and provide meaningful recommendations tailored to users’ academic interests.

A Theses and Dissertation Recommendation System addresses this challenge by leveraging modern data mining, natural language processing (NLP), and machine learning techniques to suggest academic documents that align closely with a user’s field of study, research topic, or previously accessed materials. Such a system enhances research efficiency, fosters academic collaboration, and assists students in discovering relevant works that may otherwise remain hidden in vast digital archives.

Traditional academic search engines often rely heavily on keyword matching and are limited in their ability to interpret the context or semantic relevance of a research work. This can lead to either an overwhelming number of unrelated results or a lack of precision. By contrast, a recommendation system—especially one utilizing techniques such as content-based filtering, collaborative filtering, or hybrid models—can learn from user behavior, preferences, and document features to offer more accurate, personalized suggestions.

The motivation behind developing a Theses and Dissertation Recommendation System is rooted in the increasing demand for intelligent academic tools that support literature reviews, topic exploration, and research formulation. Whether it is a graduate student looking for related works to cite or a researcher aiming to find studies with similar methodologies or results, such a system becomes a crucial asset in the academic workflow.

This study aims to design and implement a recommendation system that can automatically analyze academic theses and dissertations and generate suggestions based on the user’s research profile or input. The system will be evaluated based on its accuracy, usability, and impact on academic productivity. By providing a more intuitive and efficient way of navigating scholarly content, this project contributes to the broader goal of promoting knowledge discovery and academic excellence.