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

Automated summary

Collaborative filtering is the most popular method for addressing information overload in E-Commerce. Traditional collaborative filtering predicts the target item based on ratings from similar users. However, similarity calculation in sparse datasets may lead to decreased performance. This study proposes a new approach that considers item features to improve accuracy, using ratings from individuals with the most similar features instead of relying on the wisdom of the crowd.