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 candidate pairs (a, b) from A × B, using sampling strategies to balance coverage, diversity, and computational cost. The affinity model assigns a numeric score reflecting the predicted strength or likelihood of a pairing, learned from data through supervised, semi-supervised, or self-supervised objectives. The optimization engine updates parameters to minimize a loss function, with regularization and constraint handling.

In practice, PairAB trains on datasets of observed or plausible pairs and uses the resulting model to score or rank new candidates. It can incorporate side information about elements of A or B, employ batching and parallelization for scalability, and support evaluation via cross-validation and hold-out tests.

Applications span recommender systems (matching users to items), drug discovery (pairing compounds with targets), materials science (combining components to form functional materials), and social network analysis (predicting potential connections). The approach emphasizes modularity, interpretability of pair scores, and compatibility with standard machine learning pipelines.

Limitations include data sparsity when observed pairings are rare, potential biases in sampling, and challenges related to interpretability when the affinity model is complex. PairAB variants may differ in supervision level, sampling strategy, or loss function, reflecting domain needs.