Research On Collaborative Filtering Recommendation System Based On Expert Pool

Nowadays, the sites built with web2.0technology are very common. The web2.0means information sharing, interoperability, user-centered design, and collaboration on the Internet. A web2.0site allows users to interact and collaborate with each other. Users are not only the viewers of the contents and also the creators of contents. The data is increased so rapidly that the users are overwhelmed. The result is that users get less information than before, although the mount of data is bigger. So some technologies used for filtering data are developed. Then collaborative filtering is used very much more. This approach suffers from several shortcomings, including data sparsity and noise, the cold-start problem, and scalability. In this work, I present a novel method for recommending items to users based on expert opinions. This method is a variation of traditional collaborative filtering:rather than applying a nearest neighbor algorithm to the user-rating data, predictions are computed using a set of expert neighbors from an independent dataset, whose opinions are weighted according to their similarity to the user, This method promises to address some of the weaknesses in traditional collaborative filtering, while maintaining comparable accuracy. We validate our approach by predicting a subset of the Netflix data set. We use ratings crawled from a web portal of expert reviews, measuring results both in terms of prediction accuracy and recommendation list precision. Finally, we explore the ability of our method to generate useful recommendations, by reporting the results of a user-study where users prefer the recommendations generated by this approach.