An Enhancement of Item-based Collaborative Filtering Utilizing K-Nearest Neighbors and Interquartile Range Theory

The Item-based Collaborative Filtering Technique is a recommendation algorithm that recommends things based on the similarity between items. This study will focus enhancing concerning diversity of recommendations. paper introduces an enhanced version in which K-Nearest Neighbors and Interquartile Range Theory was implemented, wherein this diversifies final list recommendations to user. These methods prevent researchers from recommending items narrow spectrum users' interests. Compared typical IBCF, shows used effectively make recommended diversified.