# Recency Ranking by Diversification of Result Set

arXiv: https://arxiv.org/abs/2401.14595 · HTML: https://mlsanity.com/paper/2401.14595/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2024.

## Built on by (most cited first, 5 of 5)

- [It's High Time: A Survey of Temporal Question Answering](https://mlsanity.com/paper/2505.20243/) (2025)
- [Towards Effective Time-Aware Language Representation: Exploring Enhanced Temporal Understanding in Language Models](https://mlsanity.com/paper/2406.01863/) (2024)
- [RAG-Enhanced Large Language Models for Dynamic Content Expiration Prediction in Web Search](https://mlsanity.com/paper/2605.13052/) (2026)
- [Re3: Learning to Balance Relevance & Recency for Temporal Information Retrieval](https://mlsanity.com/paper/2509.01306/) (2025)
- [How often do Answers Change? Estimating Recency Requirements in Question Answering](https://mlsanity.com/paper/2603.16544/) (2026)

## Abstract (arXiv)

In this paper, we propose a web search retrieval approach which automatically detects recency sensitive queries and increases the freshness of the ordinary document ranking by a degree proportional to the probability of the need in recent content. We propose to solve the recency ranking problem by using result diversification principles and deal with the query's non-topical ambiguity appearing when the need in recent content can be detected only with uncertainty. Our offline and online experiments with millions of queries from real search engine users demonstrate the significant increase in satisfaction of users presented with a search result generated by our approach.