Advanced Clustering Techniques with Bio-Inspired for Collaborative Filtering Recommendation Systems

Clustering methodologies are pivotal in enhancing the recommendation systems powered by collaborative filtering (CF). These systems commonly rely on CF approaches to generate recommendations based on similarities. While conventional user clustering methods are prevalent, there's a growing necessity to delve into bio-inspired clustering techniques to elevate the recommendation generation process. This paper introduces a novel ensemble method termed Bio-Inspired Clustering Collaborative Filtering (BICCF) designed explicitly for recommendation systems. By harnessing swarm intelligence, this approach aims to refine the precision of recommendations within user-based CF frameworks. The study conducts experiments using real-world datasets sourced from MovieLens to assess the efficacy of this proposed method. The findings reveal marked enhancements in accuracy and efficiency, as evaluated through metrics such as Recall, Precision, MAE, and RMSE surpassing the performance of established baseline methods.