Content based news recommendation system based on fuzzy logic

Fuzzy logic is an approach that helps in computing based on “degrees of truth” rather than the usual “true or false” (1 or 0) Boolean logic. Recommender systems represent user preferences for the purpose of suggesting items to read or browse. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. Our method implements fuzzy logic to find a set of articles related to other articles which can be recommended to a reader. There is a simple reason behind using fuzzy logic. Related or recommendable news articles are not easily translated into the absolute terms of 0 and 1. We cannot absolutely point out a certain article `X´ and say that it is related to `Y´. That is why we tried to develop a fuzzy system from several attributes of a news article which will eventually describe whether an article is worth for recommendation to a user or not.