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A Personalized Hybrid Web Recommender System

Personalized recommender system has attracted wide range of attention among researchers in recent years. There has been a huge demand for development of web search apps for gaining knowledge pertaining to user‟s choice. A strong knowledge base, type of approach for search and several other factors make it accountable for a good personalized web search engine. This paper presents the state of art, challenges and other issues in this context, thereby providing the need for an improved personalized system. The paper describes an approach integrating the news feeds and users opinion on web news using content, collaborative and hybrid approaches. Experiments carried out shows the effectiveness of the proposed system using popular dataset such as MovieLens.