Hybrid Recommender System with Similarities and Associations among Users

Abstract

Even if there is a lot of information available online, the user is still faced with the challenge of choosing the information that is relevant to him or her. Recommender Systems (RS) were created in order to identify the information of our interest using information filtering to tackle this issue. RS assists in locating the pertinent data for a user from a vast collection. There are various RS types, but those created using collaborative filtering approaches have proven to be the most effective for a variety of issues. One of the most popular RS accessible is the one for recommending movies. Here, recommendations will be made based on users' and objects' shared attributes. The user objects and item objects in the movie recommendation system are both very numerous. To offer effective  recommendations, this research integrates the Collaborative Filtering (CF) technique with user associations. Therefore, the generated recommendations may be used to provide user recommendations. Despite the lack of data, this hybridized model is able to provide recommendations that are beneficial. In this article, user associations and CF approaches are combined to make movie recommendation suggestions.