Designing a Hybrid Approach for Web Recommendation Using Annotation

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Author(s): Sunny Sharma (University of Jammu, Kathua, India), Vijay Rana (Dr. Yashwant Singh Parmar University of Horticulture and Forestry, India)and Vivek Kumar (Arni University, India)
Copyright: 2022
Pages: 14
Source title: Applications of Computational Science in Artificial Intelligence
Source Author(s)/Editor(s): Anand Nayyar (Duy Tan University, Da Nang, Vietnam), Sandeep Kumar (CHRIST University (Deemed), Bangalore, India)and Akshat Agrawal (Amity University, Guragon, India)
DOI: 10.4018/978-1-7998-9012-6.ch011

Keywords: Artificial Intelligence / Computational Intelligence / Computer Science & IT / Information Science Reference

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Abstract

Web recommendation is the process of providing personalization recommendations to the requirements of specific users. A lot of research is conducted on recommender systems by a broad range of communities including computer scientists and interdisciplinary researchers. In response, a lot of recommender systems have been developed so far. However, the complexity of these systems can lead to information overload and decreased utility for the users. For these reasons, researchers have sought to apply the techniques of recommender systems to deliver personalized views of social annotation systems. In this chapter, the authors cover recent improvements in recommender systems and explore the major challenges. This chapter finally presents a prototype for adaptation to end-users. The model emphasizes an annotation-based recommendation system for generating recommendations.

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Designing a Hybrid Approach for Web Recommendation Using Annotation

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