To personalize or not to personalize: modeling queries with variation in user intent

By Jaime Teevan, Susan T. Dumais, Daniel J. Liebling. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2008

334 citations recorded by the source corpus.

Subfields: Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/9d99b61cf09a268d975e63f2e9193323536ae6be

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. Ryen W. White
2. Jaime Teevan
3. Donna K. Harman
4. Susan T. Dumais
5. Eric Horvitz
6. Paul N. Bennett
7. Peter Bailey
8. Wei Chu

In Signal Processing

1. Donna K. Harman
2. Susan T. Dumais
3. Jaime Teevan
4. Paul N. Bennett
5. Eric Horvitz
6. Ryen W. White
7. Wei Chu
8. Li Deng

Citations in the corpus

• Potential for personalization (2010, background): “For some queries there was a large gap between the individual and group curves, and for other queries the difference was much smaller, and we have begun to ex­amine this in greater detail [Teevan et al. 2008].”
• Information Retrieval Evaluation (2011, methodology): “The search log experiments rely on many types of information, with new types continuing to be tried (see [175] for an excellent example of this).”
• Modeling the impact of short- and long-term behavior on search personalization (2012, methodology): “[30]).”
• Characterizing the influence of domain expertise on web search behavior (2009, background): “[18] we show that some queries have more variation in user intent (defined by clicked results) than others.”