Advanced Machine Learning Approaches for Secure CloudBased Recommendation Systems with Computational Optimization and Cryptographic Security

Modern cloud-based recommendation systems face critical challenges in balancing computational efficiency, security, and accuracy. This research presents three innovative approaches to address these challenges: (1) A Novel Secure Friend Recommendation in Cloud (NSKD-RS) employing cryptographic optimization, (2) Friend Recommendation System using Crow Search Optimization with Adaptive NeuroFuzzy Inference System (CSO-ANFIS), and (3) Content-Based Movie Recommendation System utilizing Monarch Butterfly Optimization (MBO) with Deep Belief Networks (DBN). Through comprehensive simulation and evaluation on real-world datasets, the proposed NSKD-RS model reduces computational complexity by 60% in key generation, 78% in encryption, and 81% in decryption compared to baseline GMS-MSN approaches. The CSO-ANFIS framework achieves 95.75% accuracy in friend recommendation tasks, surpassing traditional PSO and GA algorithms. The MBO-DBN content-based movie recommendation system demonstrates superior performance with 97.35% precision and 96.60% recall, outperforming existing deep learning models. This research integrates advanced machine learning techniques, reinforcement learning optimization, natural language processing for semantic analysis, cloud security protocols, and generative AI principles to create scalable, secure, and efficient recommendation systems suitable for enterprise cloud environments. Experimental validation on Facebook and MovieLens datasets confirms the effectiveness and generalizability of the proposed approaches.

Keywords: Cloud Security, Recommendation Systems, Machine Learning, Optimization Algorithms, Cryptographic Protocols, Deep Learning, Artificial Intelligence, Computational Complexity