OPTIMIZING RECOMMENDATION SYSTEMS USING GRAPH-BASED METHODS AND MACHINE LEARNING ALGORITHMS
Abstract
Recommendation systems must rank relevant items from sparse, dynamic and policy-shaped interaction data. This paper presents an evidence-bounded framework that combines a bipartite user-item graph, a predictive machine-learning score and an adaptive policy value within one top-K ranking interface. Graph propagation captures higher-order connectivity; supervised or deep models incorporate interaction and side information; and reinforcement learning represents delayed utility when reliable feedback is available. The revision formalizes graph construction, message passing, score fusion, policy updating, pairwise ranking loss and top-K evaluation through ten editable equations, a notation table and an IEEE/ACM-style algorithm. Four requested figures and the wider visual set are rebuilt without watermarks. Because the source manuscript provides no raw run-level logs or reproducible benchmark outputs, the paper makes no new empirical-superiority claim. Instead, it specifies a leakage-aware evaluation protocol, computational complexity, deployment controls, and ethical requirements for privacy, fairness, transparency and human oversight.
Keywords
Recommendation systems, graph-based methods, graph neural networks, machine learning, reinforcement learning, top-K ranking, data sparsity, cold start.
