suggesters
Suggesters are systems or components that generate predicted items, phrases, or actions to present to a user in real time. They aim to assist discovery, input, and decision making across interfaces such as search boxes, e-commerce sites, social platforms, and productivity tools. While they share techniques with broader recommender systems, suggesters often focus on immediate relevance to the current context and produce single-turn or short-list outputs.
Common types of suggesters include query suggestions and autocomplete, which propose possible search terms or corrections
Suggesters rely on diverse data sources, including historical user interactions, item metadata, and contextual signals such
System design typically features a fast, multi-stage pipeline: candidate generation to produce a broad set of
Evaluation uses both offline and online methods. Offline metrics include precision@k, recall@k, and mean reciprocal rank,
Privacy and ethics considerations include data collection scope, consent, anonymization, and minimizing exposure of sensitive data.