Recommender system based on collaborative filtering

Keywords: collaborative filtering, recommender systems, user preferences, similarity measure, cosine similarity, user-based approach, item-based approach, rating prediction, movie recommendation, Netflix Prize, RMSE, MAE, precision, recall, F1-score, user engagement, user experience

Abstract

Collaborative filtering is a popular technique for providing personalized recommendations in recommender systems. However, the sparsity problem and the accuracy-diversity tradeoff are major challenges that limit its performance. In this article, we propose a novel approach that combines matrix factorization with novelty metrics to improve the accuracy and diversity of recommendations. We evaluate our approach on the MovieLens dataset and compare it with several state-of-the-art techniques, including neighborhood-based methods, probabilistic models, and hybrid approaches. Our experimental results show that our method is better than other techniques in terms of both accuracy and diversity, as measured by precision, recall, and novelty metrics.

References

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Published
2023-12-16
How to Cite
Yurchak, I., & Hryhlevych , M. (2023). Recommender system based on collaborative filtering. COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, (53), 78-85. https://doi.org/10.36910/6775-2524-0560-2023-53-12
Section
Computer science and computer engineering