User Location and Collaborative based Recommender System using Naive Bayes Classifier and UIR Matrix

Abstract

The world is filled with information and getting the right information is a challenging task for internet users and online buyers. Recommender system helps internet users to get their information in a short span of time. It acts as an information extraction system that works behind users to perform their search easier. The recommender system comes under user’s content or item based search, similar users browsing behavior called collaborative and combination of both known as a hybrid. Here collaborative-based approach is adopted which recommends items to their users based on their past browsing behavior. In this article, the User-Item-Rating matrix is formulated concerning user personal profile, rating of the product, and reviews given by the users during their previous browsing history. In this research, user location is considered as an important attribute to group similar users. It also attempts to suppress the scalability and sparsity problems of the traditional collaborative filtering approach. So, the User-Item-Rating (UIR) matrix has considered the location, ratings and reviews for future recommendation. The Navie Bayes classifier algorithm is used to provide accurate topmost recommendations to internet users. The data set is taken from the MovieLens and IMDb database. The accuracy of the recommender system is measured based on the main metric f-measure. The experimental result has proven the improvement of the recommender system with the mentioned added attributes.