Dynamic feature weighting in hybrid recommendation systems using genetic algorithm

This paper presents an improved recommendation system based on a compact hybrid user model by incorporating a Genetic Algorithm (GA) to optimize feature weights.Building upon our previous work, which introduced a hybrid recommendation model integrating demographic information, genre interest indicators (GII), and fuzzy logic to address data sparsity and enhance user similarity computations, we identify and resolve a key limitation: the equal weighting of all user model features.In reality, users place varying importance on different attributes, and these preferences naturally evolve over time.To effectively address this dynamic nature of user preferences, we propose using a Genetic Algorithm to dynamically optimize weight coefficients, enabling the model to adaptively prioritize user attributes during similarity computation.The proposed GA-based weighting mechanism ensures continuous adaptation to user behavior changes, thereby enhancing recommendation relevance and accuracy.Experimental evaluations on the MovieLens dataset confirm the superiority of the GA-enhanced approach, demonstrating significant improvements in recommendation accuracy compared to traditional hybrid methods.