CUPCF: combining users preferences in collaborative filtering for better recommendation

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.

A study on the adaptive technology of intelligent learning system based on mobile terminal recognition

“…In the aspect of adaptive learning system, Thorat et al [6] has used collaborative filtering technology to explore the feedback information of learners to learning resources, and realized personalized recommendation system. There are other learning system that have adopted collaborative filtering recommendation technology, such as Movie Recommendation System (MRS) [7], CUPCF [8], etc. Holland Open University has made a deep research on personalized learning recommendation system, and that the research on personalized recommendation system is an important part of the EU TENCompetence project.…”

ResNetMF: Enhancing Recommendation Systems with Residual Network Matrix Factorization

“…Collaborative filtering, considered one of the most widely utilized methods in building recommendation systems [10], relies on historical data of items or users to generate recommendations [11]. This approach operates under the assumption that users with similar preferences in the past are likely to have similar preferences in the future [12]. Collaborative filtering utilizes rating information to identify like-minded users and computes forecasts for the current user based on their patterns.…”

Untitled

“…Among these techniques, solid-state thermal decomposition method was found to be simple, easy, low cost, and without FULL PAPER Asian Journal of Nanoscience and Materials usage solvent or surfactant [13][14][15][16][17][18][19][20]. In our group, we have been interested in the synthesis and characterization of the Co3O4 nanoparticles [29,30]…”