The development of the internet and technology has caused a huge increase in data that has become untenable for the user. Solving this information overflow has become a main motivation for creating recommender systems. Recommender systems make user’s decision-making easier by replacing user’s information discovery process. Recommender will suggest a personalized recommendation based on behavior and personality, resulting in items that could be interesting to the user. This recommendation reduces a large amount of data that is irrelevant to the user.
Our method is focusing on domain Yelp.com, where are people rating businesses and places. This domain suffer with data sparsity, because users tend to not leave ratings for many places. We are trying to reduce this sparsity and improve recommendation with hybrid system, based on clusterization with k-Nearest Neighbours algorithm.