Movie Recommendation System based on user’s search history using incremental clustering

In today’s world as the technology is advancing there has been a drastic increase in the number of websites, huge amounts of data have been generated and endless options emerged to choose from when it comes to movies. When there are several options available, one might be generally confused in making their choices. To avoid that, recommender systems are the ones that plays a major part in making recommendations of the movies to the users, by using some algorithms and techniques. These recommender systems are used for various purposes like, in recommending music, movies, products, research papers, youtube, temporal recommendation and so on. A recommender System gives suggestions based majorly on the user’s changing interest over time or the user searching patterns using some algorithms like Clustering, and suggests the movies based on the generated interests of the user in the course oftime

I. Introduction

The introduction of recommender systems could be traced back to 1979 with relation to cognitive science. Recommender systems are used more widely than other areas like approximation theory information retrieval, forecasting theories. The common problem faced by users is the information overloading, which means to have various options for a single domain or interest. To avoid this problem recommender systems, in the recent years are great in handling such problems. To resolve this, rs (recommender systems) guide user’s towards new and unknown interests that may be relevant to the user’s current task.