CUPCF: Combining Users Preferences in Collaborative Filtering for Better Recommendation

TL;DR

CUPCF is a new collaborative filtering approach that combines similarity measures to improve recommendation accuracy, especially addressing cold start and data sparsity issues, demonstrated on MovieLens data.

Contribution

It introduces a novel similarity combination method in collaborative filtering to enhance prediction accuracy and reduce error rates in recommender systems.

Findings

Maximum error rate improvement of 15.5%

Accuracy, Precision, and Recall up to 0.914, 0.91436, and 0.9974

Effective in cold start and sparse data scenarios

Abstract

How to make the best decision between the opinions and tastes of your friends and acquaintances? Therefore, recommender systems are used to solve such issues. The common algorithms use a similarity measure to predict active users' tastes over a particular item. According to the cold start and data sparsity problems, these systems cannot predict and suggest particular items to users. In this paper, we introduce a new recommender system is able to find user preferences and based on it, provides the recommendations. Our proposed system called CUPCF is a combination of two similarity measures in collaborative filtering to solve the data sparsity problem and poor prediction (high prediction error rate) problems for better recommendation. The experimental results based on MovieLens dataset show that, combined with the preferences of the user's nearest neighbor, the proposed system error rate compared to a number of state-of-the-art recommendation methods improved. Furthermore, the results indicate the efficiency of CUPCF. The maximum improved error rate of the system is 15.5% and the maximum values of Accuracy, Precision and Recall of CUPCF are 0.91402, 0.91436 and 0.9974 respectively.