| Abstract: | Artificial Intelligence (AI) has witnessed remarkable advancements in recent years, catalyzing
transformative changes across various domains. Within this landscape, recommendation
systems have emerged as a cornerstone of AI, revolutionizing the way machines
understand and predict user preferences. These systems play a pivotal role in various
applications, ranging from e-commerce and content streaming to personalized marketing
and social media engagement.
This thesis conducts a comprehensive examination of interactive and sequential recommendation
systems, analyzing their state-of-the-art methodologies. By delving into the
intricacies of these systems, the study explores advanced techniques such as reinforcement
learning, neural collaborative filtering, and knowledge graph integration. The comparative
analysis highlights key strengths and weaknesses of existing approaches, providing a
thorough understanding of the current landscape of recommendation technologies.
By shedding light on the challenges and limitations of existing recommendation systems,
this thesis aims to contribute to a deeper understanding of their potential applications
and implications. The findings underscore the importance of continued innovation in
this field and provide actionable insights for future research directions. These insights
are geared towards enhancing the utility of recommendation systems in various domains,
ultimately leading to more intuitive and effective user experiences.
This research serves as a comprehensive resource for scholars and practitioners, encouraging
the exploration of novel methodologies and the integration of emerging technologies
to push the boundaries of what recommendation systems can achieve. |
