Integrating Collaborative Filtering Technique Using Rating Approach to Ascertain Similarity Between the Users

Abstract

The recommender system handles the plethora of data by filtering the most crucial information based on the dataset provided by a user and other criterion that are taken into account.(i.e., user's choice and interest). It determines whether a user and an item are compatible and then assumes that they are similar in order to make recommendations. Recommendation system uses Singular value decomposition method as collaborative filtering technique. The objective of this research paper is to propose the recommendation system that has an ability to recommend products to users based on ratings. We collect essential information like ratings given by the users from e-commerce that are required for recommendation, Initially the dataset that are gathered are sparse dataset, cosine similarity is used to find the similarity between the users. Subsequently, we collect non-sparse data and use Euclidian distance and Manhattan distance method to measure the distance between users and the graph is plotted, this ensures the similar liking and preferences between them. This method of making recommendations are more reliable and attainable.