Mining Query Logs: Turning Search Usage Data into Knowledge

By Fabrizio Silvestri. 2010

202 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, 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 Computer Networks and Communications

1. Benjamin Livshits
2. Fernando Diaz
3. Carlos Castillo
4. Brian D. Davison
5. Aleksandra Korolova
6. Emre Kıcıman
7. Jun Xu
8. Alexandra Olteanu

In Information Systems

1. Jun Xu
2. Hang Li
3. Carlos Castillo
4. Fernando Diaz
5. Alexandra Olteanu
6. Benjamin Livshits
7. Brian D. Davison
8. Aleksandra Korolova

In Signal Processing

1. Benjamin Livshits
2. Fernando Diaz
3. Carlos Castillo
4. Brian D. Davison
5. Aleksandra Korolova
6. Emre Kıcıman
7. Jun Xu
8. Hang Li

Citations in the corpus

• BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model (2017, background): “Such caching of the most frequently used queries locally has a disproportionately positive impact on the expected query latency [36, 3] as queries to a search engine follow a power-law distribution [4].”
• Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Studying web search behavior, Silvestri (2010) found that it varies across semantic domains, and Aula et al. (2010) that it changes as the task at hand becomes more difficult.”
• Adversarial Web Search (2011, background): “This incident and its implications are described in [211].”
• Semantic Matching in Search (2014, background): “(For a survey on search log mining, see [163, 94].”