Search result diversification

324 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Diversity in Big Data: A Review (2017, unknown): “Although there is no single formal definition of div, the various measures can be roughly classified as (a) distance based, (b) coverage based, and (c) novelty based.(35)”
Recommendation systems: Principles, methods and evaluation (2015, background): “Precision is the fraction of recommended items that is actually relevant to the user, while recall can be defined as the fraction of relevant items that are also part of the set of recommended items [87].”
Managing Popularity Bias in Recommender Systems with Personalized Re-ranking (2019, background): “Result diversification has been studied in the context of information retrieval, especially for web search engines, which have a similar goal to find a ranking of documents that together provide a complete coverage of the aspects underlying a query (Santos et al. 2015).”
FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents (2025, unknown): “However, diversity in search [6, 66, 85] penalizes information redundancy within retrieved documents to enrich information content and improve efficiency.”