A Novel Recommender System using Trust based K-Mean Method

Abstract

The Recommended systems (RS) are designed to help and guide users in finding their desired items from large-scale datasets such as the internet. Recommender systems are one of the main tools to overcome the problem of information overload. Collaborative filtering (CF) is one of the best approaches for recommender systems and are spreading as a dominant approach. However, they have the problem of cold-start and data sparsity. Trust-based approaches try to create a neighbourhood and network of trusted users that demonstrate users’ trust in each other’s opinions. As such, these systems recommend items based on users’ relationships. In the proposed method, we try to resolve the problems of low coverage rate and high RMSE rate in trust-based recommender systems using Mk-means clustering. For clustering data, the Mk-means method has been used on MovieLens dataset and the rating matrix is calculated to have the least overlapping.