A framework for high-relevance reciprocal matching in mentorship platforms
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[EMBARGOED UNTIL 12/01/2026] Mentorship is a critical component of professional and academic development, yet opportunities are often limited by personal networks and chance encounters, disproportionately affecting underrepresented students. Existing digital matching platforms frequently rely on restrictive keyword-based searches, which fail to capture the semantic nuance of a candidate's skills and interests, leading to suboptimal matches and continuing systemic biases. This thesis aims to address these challenges by designing, implementing, and rigorously evaluating a novel, reciprocal matching system to connect mentees and mentors with greater accuracy. The proposed solution is a two-stage hybrid recommendation algorithm. The first stage employs semantic search, utilizing transformer-based vector embeddings to represent the rich, unstructured text of user profiles and identify a comprehensive set of conceptually relevant candidates. The second stage uses Reciprocal Rank Fusion, a score-agnostic method, to merge and re-rank candidates from multiple retrieval signals, prioritizing those with consistent high rankings across different models. The system's efficacy is validated through a comprehensive two-phase evaluation framework. An offline analysis compares the proposed algorithm against traditional keyword-based and content-based filtering models using rank-aware metrics. Subsequently, an online A/B test measures real-world impact on user behavior, with the primary success metric defined as the Reciprocal Acceptance Rate. Offline analysis showed the hybrid model achieved superior precision and recall, and the subsequent online A/B test confirmed its practical superiority. The hybrid algorithm yielded statistically significant improvements over the baseline, including a +121.3% lift in Reciprocal Acceptance Rate and a 100% reduction in Empty State Frequency. This work contributes a scalable and more equitable framework for mentor matching, demonstrating that a semantic, multi-faceted approach can successfully bridge the mentorship gap and foster more meaningful professional connections
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M.S.