Personalized news recommendation using hybrid filtering techniques

Abstract

Ever increasing volume of data on the Web has resulted in information explosion. One of the major challenges faced by a user is to find relevant information catering to their interests. To alleviate this problem recommender system is used to search for and filter information based on the user s interest. In the news domain, recommendation technique particularly aims at collecting news articles according to the user s interests with the objective of creating a personal newspaper. The popularity of news recommender systems is attributed to availability and easy accessibility of news from websites like Google and Yahoo. However, the driving problem is to identify and recommend the most interesting news articles to each user balancing user interest with importance of the news. Some of the challenges faced by the news domain include dynamic nature of the news domain, changing user interests, popularity and novelty of the news, lack of history information for new users and the volume of information. These challenges result in cold-start, sparse data situations along with scalability and overspecialization problems. The major goal of this research is to design and develop a personalized news recommender system capable of handling data sparsity, cold-start, dynamically changing user newline interests and scalability issues. Experiments are performed on NEWS and YOW datasets newline newline