1 research outputs found
Collaborative Personalized Web Recommender System using Entropy based Similarity Measure
On the internet, web surfers, in the search of information, always strive for
recommendations. The solutions for generating recommendations become more
difficult because of exponential increase in information domain day by day. In
this paper, we have calculated entropy based similarity between users to
achieve solution for scalability problem. Using this concept, we have
implemented an online user based collaborative web recommender system. In this
model based collaborative system, the user session is divided into two levels.
Entropy is calculated at both the levels. It is shown that from the set of
valuable recommenders obtained at level I; only those recommenders having lower
entropy at level II than entropy at level I, served as trustworthy
recommenders. Finally, top N recommendations are generated from such
trustworthy recommenders for an online user.Comment: 10 page