Recommendation System Based on a Compact Hybrid User Model Using Fuzzy Logic Algorithms
Nina Khairova, Nataliia Sharonova, Dmytro Sytnikov, Mykyta O. Hrebeniuk, Polina E. Sytnikova · 2024
The paper presents algorithm designed to address the challenges of traditional collaborative filtering methods by integrating a compact hybrid user model.This model incorporates hybrid features, demographic information, and fuzzy logic principles to improve recommendation accuracy.A key contribution of this work is the development of an innovative approach for calculating user similarity using fuzzy logic algorithms.By considering fuzzy concepts, the proposed approach effectively captures the inherent uncertainty and imprecision in user preferences, leading to more nuanced and accurate recommendations.Experimental evaluations conducted on the widely used MovieLens dataset provide insights into the performance of the proposed algorithm compared to traditional collaborative filtering techniques such as Pearson correlation and cosine similarity.The dataset, which contains both user ratings and demographic details, serves as a comprehensive testbed for assessing recommendation systems.The results of the experiments demonstrate the superiority of the proposed approach in capturing user similarities and enhancing recommendation accuracy.This paper contributes to the ongoing progress in recommendation systems by proposing a solution that addresses the challenges associated with traditional collaborative filtering methods.Through the integration of hybrid user models, demographic data, and fuzzy logic principles, the proposed algorithm offers a promising approach for enhancing recommendation accuracy across diverse application domains.