Overretrieve

Overretrieve is a property of a retrieval system in which the number of returned items exceeds the number necessary to satisfy the user's information need, often including a substantial portion of non-relevant results. In information retrieval, it is primarily a concern of precision, the proportion of retrieved items that are relevant, though it can also affect user experience and system efficiency.

Causes of overretrieve include broad or vague queries, overly permissive matching criteria, ranking functions that prioritize recall over precision, default top-N retrieval without user-controlled limits, and domain-specific data with dense overlaps of features. Overretrieve commonly coexists with under-retrieve if the system tries to increase recall by broadening matching when relevance estimations are uncertain.

Effects include increased bandwidth and processing costs, longer latency, greater cognitive load for users who must skim through many irrelevant results, and diminished perceived quality of the search system. In practice, operators measure precision and recall, precision-at-k or average precision, to assess and monitor overretrieve.

Mitigation strategies include tighter query understanding and expansion that respect user intent, result cutoff mechanisms such as top-k limits, post-retrieval filtering, relevance feedback, and more discriminating ranking models. System designers may also employ user-specific constraints, domain filters, or content-type filters to reduce overretrieve.

Overretrieve is related to but distinct from overfetching in APIs and databases, where additional fields or items are retrieved beyond what the client requires. In all cases, the goal is to align retrieved content with the user’s actual needs to optimize efficiency and satisfaction.