Towards Exploring Personalized Hyperlink Recommendations Through Machine Learning
Authors: 
Agorakis Bompotas, Panagiotis Triantafyllopoulos, 
George E. Raptis, Christina Katsini, Christos MakrisAuthors Info & Claims
Agorakis Bompotas, Panagiotis Triantafyllopoulos, George E. Raptis, Christina Katsini, Christos MakrisAuthors Info & ClaimsPages 528 - 533
Abstract
The Internet offers a wealth of content, making it increasingly difficult for users to navigate website information. The volume of hyperlinks on a website often leaves users struggling with content overload, hindering their ability to find relevant information of high interest. This problem highlights the critical need for tools to improve the user experience by providing personalized hyperlink recommendations on a specific website. This paper introduces HypeRec, a browser extension that attempts to address this problem by leveraging and comparing different machine learning and recommendation algorithms to guide users to content consistent with their interests and preferences. Our approach involves extracting hyperlinks from a webpage and subjecting the corresponding textual content to natural language processing techniques. In this way, it simplifies the users’ navigation within a website and promotes a more intuitive and satisfying web browsing experience.
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- Towards Exploring Personalized Hyperlink Recommendations Through Machine Learning
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Published In
June 2024
662 pages
ISBN:9798400704666
DOI:10.1145/3631700
- General Chairs:
- Ludovico Boratto,
- Cristina Gena,
- Mirko Marras,
- Program Chairs:
- Panagiotis Germanakos,
- Elvira Popescus
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Published: 28 June 2024
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UMAP '24: 32nd ACM Conference on User Modeling, Adaptation and Personalization
July 1 - 4, 2024
Cagliari, Italy
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