This document summarizes a research paper on optimal feature selection for context-aware recommendation systems using differential relaxation. The paper proposes a differential context relaxation (DCR) model that applies different context relaxations to different components of a recommendation algorithm to maximize their contributions. It uses binary particle swarm optimization to efficiently find optimal context relaxations and outperforms exhaustive search. Experimental results on a food preference dataset show the effects of different contexts and context-linked features. The paper discusses limitations and opportunities for future work to address sparsity issues.