Utilizing Alike Neighbor Influenced Similarity Metric for Efficient Prediction in Collaborative Filter-Approach-Based Recommendation System

The most popular method collaborative filter approach is primarily used to handle the information overloading problem in E-Commerce. Traditionally, filtering uses ratings of similar users for predicting target item. Similarity calculation sparse dataset greatly influences predicted rating, as less count co-rated items may degrade performance filtering. However, consideration item features find nearest neighbor can be a more judicious increase proportion users. In this study, we offer new paradigm raising rating prediction accuracy proposed framework rated feature ’most’ individuals, instead using wisdom crowd. reliability evaluated on static MovieLens datasets and experimental results corroborate our anticipations.