Towards recency ranking in web search

By Anlei Dong, Yi Chang, Zhaohui Zheng, Gilad Mishne, Jing Bai, Ruiqiang Zhang, Karolina Buchner, Ciya Liao, Fernando Diaz. 2010

205 citations recorded by the source corpus.

Subfields: Information Systems, Signal Processing

How researchers see this paper

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

In Information Systems

1. Lihong Li
2. Olivier Chapelle
3. Jaime Teevan
4. Eric Horvitz
5. Yi Chang
6. Susan T. Dumais
7. Ankur Gupta
8. Shunbao Chen

In Signal Processing

1. Yi Chang
2. Susan T. Dumais
3. Tao Mei
4. Jaime Teevan
5. Yong Rui
6. Eric Horvitz
7. Winston H. Hsu
8. Olivier Chapelle

Citations in the corpus

• Counterfactual Estimation and Optimization of Click Metrics for Search Engines (2014, background): “gests the opposite|users who issue that query are often more interested in news about the actor, not the ocial website.1 Second, in some applications like personalized search [25] and recency search [11], judges simply lack the information to provide sensible labels. Third, user experience with a se”
• Refining Recency Search Results with User Click Feedback (2011, methodology): “Previous work [8] introduced query classifiers to detect time-sensitive queries, implemented time-varying features that reflect document freshness, and recency demoted labels provided by human editors.”
• Yahoo! Learning to Rank Challenge Overview (2010, background): “More information on such features can be found in (Dong et al., 2010, Section 5.1).”
• Learning to Rank for Information Retrieval and Natural Language Processing (2011, unknown): “Learning to rank is also applied to different problems in web search, including context aware search [105], recency ranking [31], federated search [79], personalized search, online advertisement, etc.”
• Modeling and predicting behavioral dynamics on the web (2012, background): “[1] developed algorithms for identifying queries that are related to breaking news and for blending relevant news results into core search results.”
• Learning to personalize trending image search suggestion (2014, background): “Dong et al. investigate the detection of trending searches [10].”