Recency Ranking by Diversification of Result Set
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jan 2024.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 2.
5 papers.
- 2025It's High Time: A Survey of Temporal Question Answeringcited by 12
- 2024Towards Effective Time-Aware Language Representation: Exploring Enhanced Temporal Understanding in Language Modelscited by 5
- 2026RAG-Enhanced Large Language Models for Dynamic Content Expiration Prediction in Web Searchcited by 1
- 2025Re3: Learning to Balance Relevance & Recency for Temporal Information Retrievalcited by 0
- 2026How often do Answers Change? Estimating Recency Requirements in Question Answeringcited by 0
- 2026RAG-Enhanced Large Language Models for Dynamic Content Expiration Prediction in Web Search13 May 2026
- 2026How often do Answers Change? Estimating Recency Requirements in Question Answering17 Mar 2026
- 2025Re3: Learning to Balance Relevance & Recency for Temporal Information Retrieval1 Sep 2025
- 2025It's High Time: A Survey of Temporal Question Answering26 May 2025
- 2024Towards Effective Time-Aware Language Representation: Exploring Enhanced Temporal Understanding in Language Models4 Jun 2024