PairAB
PairAB is a computational framework for analyzing pairwise interactions between elements drawn from two sets, A and B. It provides a structured approach to represent, score, and optimize cross-pair relationships in domains where interactions between two components are central, such as recommendation systems, chemistry, and network analysis.
Core components include a pair sampler, an affinity model, and an optimization engine. The pair sampler generates
In practice, PairAB trains on datasets of observed or plausible pairs and uses the resulting model to
Applications span recommender systems (matching users to items), drug discovery (pairing compounds with targets), materials science
Limitations include data sparsity when observed pairings are rare, potential biases in sampling, and challenges related