An equity aware recommender system for university admissions balancing operational constraints and strategic objectives

Institutions of higher education must balance multiple, often conflicting objectives when setting admission targets for their academic programs. In this paper, we introduce a recommendation system that integrates Constraint Satisfaction Problem (CSP) techniques, goal programming, and Equity Theory to optimize student assignments. Our model strictly enforces hard constraints-such as faculty-hour limits and classroom capacities-while accommodating soft constraints-such as government quotas and institutional preferences-through adjustable penalty functions. Evaluations against static and heuristic benchmarks show that our approach maintains enrollment at 85-90% of total capacity, markedly reducing both the frequency and severity of constraint violations. Furthermore, an average Gini coefficient of 0.067 demonstrates a fairer distribution of seats across programs. Over five simulated admission cycles, institutions employing this recommender achieve substantial compliance improvements within four years, striking an effective balance between rapid constraint adherence and stable enrollment figures. These results confirm that our system offers a practical, data-driven solution for flexible and equitable enrollment management in resource-limited higher-education settings.