Collaborative Personalized Web Recommender System using Entropy based Similarity Measure
This work addresses scalability issues for web surfers seeking personalized recommendations, but it appears incremental as it builds on existing collaborative filtering methods with a new entropy-based approach.
The paper tackled the scalability problem in web recommender systems by proposing an entropy-based similarity measure to identify trustworthy recommenders, resulting in a model that filters recommenders based on entropy levels to generate top N recommendations for online users.
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.