By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    construction analytics
    5 Benefits of Analytics to Manage Commercial Construction
    5 Min Read
    benefits of data analytics for financial industry
    Fascinating Changes Data Analytics Brings to Finance
    7 Min Read
    analyzing big data for its quality and value
    Use this Strategic Approach to Maximize Your Data’s Value
    6 Min Read
    data-driven seo for product pages
    6 Tips for Using Data Analytics for Product Page SEO
    11 Min Read
    big data analytics in business
    5 Ways to Utilize Data Analytics to Grow Your Business
    6 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Not All Queries Are Created Equal
Share
Notification Show More
Latest News
cloud-centric companies using network relocation
Cloud-Centric Companies Discover Benefits & Pitfalls of Network Relocation
Cloud Computing
construction analytics
5 Benefits of Analytics to Manage Commercial Construction
Analytics
database compliance guide
Four Strategies For Effective Database Compliance
Data Management
Digital Security From Weaponized AI
Fortifying Enterprise Digital Security Against Hackers Weaponizing AI
Security
DevOps on cloud
Optimizing Cost with DevOps on the Cloud
Cloud Computing Development Exclusive IT
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Visualization > Not All Queries Are Created Equal
Business IntelligenceData Visualization

Not All Queries Are Created Equal

Daniel Tunkelang
Last updated: 2010/03/07 at 4:05 PM
Daniel Tunkelang
4 Min Read
SHARE
- Advertisement -

A topic with which I developed an obsession in my last few years at Endeca is understanding how to predict query difficulty and performance–performance in the information retrieval sense meaning results quality, not computational efficiency. If only we knew how well a search engine would do–or did–in meeting the user’s information need, we might adapt the user experience to reflect our degree of confidence.

I was particularly interested in work related to the query clarity score initially proposed by Steve Cronen-Townsend, Yun Zhou, and Bruce Croft in a 2002 paper entitled “Predicting Query Performance“. But there is a wide variety of work in this area, including methods to predict performance either before or after results retrieval.

- Advertisement -

Happily, Claudia Hauff just published a dissertation on this topic, entitled “Predicting the Effectiveness of Queries and Retrieval Systems“. It is very well written, and I recommend it to anyone interested in learning more about this subject. She presents not only her own original research, but also a comprehensive analysis of others’ efforts.

Here is an excerpt from the abstract:

More Read

A year on: The promise of SAP HANA for Big Data analytics (Part Two)

Guest Post: Information Retrieval using a Bayesian Model of Learning and Generalization
Micro vs. Macro Information Retrieval
Marti Hearst’s Book on Search User Interfaces

In this thesis we consider users’ attempts to…

- Advertisement -

A topic with which I developed an obsession in my last few years at Endeca is understanding how to predict query difficulty and performance–performance in the information retrieval sense meaning results quality, not computational efficiency. If only we knew how well a search engine would do–or did–in meeting the user’s information need, we might adapt the user experience to reflect our degree of confidence.

I was particularly interested in work related to the query clarity score initially proposed by Steve Cronen-Townsend, Yun Zhou, and Bruce Croft in a 2002 paper entitled “Predicting Query Performance“. But there is a wide variety of work in this area, including methods to predict performance either before or after results retrieval.

Happily, Claudia Hauff just published a dissertation on this topic, entitled “Predicting the Effectiveness of Queries and Retrieval Systems“. It is very well written, and I recommend it to anyone interested in learning more about this subject. She presents not only her own original research, but also a comprehensive analysis of others’ efforts.

Here is an excerpt from the abstract:

In this thesis we consider users’ attempts to express their information needs through queries, or search requests and try to predict whether those requests will be of high or low quality. Intuitively, a query’s quality is determined by the outcome of the query, that is, whether the retrieved search results meet the user’s expectations. The second type of prediction methods under investigation are those which attempt to predict the quality of search systems themselves. Given a number of search systems to consider, these methods estimate how well or how poorly the systems will perform in comparison to each other.

- Advertisement -

I look forward to seeing researchers continue to build on these results, and I am excited for the day when search engines are more reflective on their own strengths and weakness.

Link to original post

TAGGED: information retrieval, query performance
Daniel Tunkelang March 7, 2010
Share this Article
Facebook Twitter Pinterest LinkedIn
Share
- Advertisement -

Follow us on Facebook

Latest News

cloud-centric companies using network relocation
Cloud-Centric Companies Discover Benefits & Pitfalls of Network Relocation
Cloud Computing
construction analytics
5 Benefits of Analytics to Manage Commercial Construction
Analytics
database compliance guide
Four Strategies For Effective Database Compliance
Data Management
Digital Security From Weaponized AI
Fortifying Enterprise Digital Security Against Hackers Weaponizing AI
Security

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

A year on: The promise of SAP HANA for Big Data analytics (Part Two)

0 Min Read

Guest Post: Information Retrieval using a Bayesian Model of Learning and Generalization

12 Min Read

Micro vs. Macro Information Retrieval

5 Min Read

Marti Hearst’s Book on Search User Interfaces

5 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Artificial Intelligence Chatbots Exclusive
data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Analytics Big Data Chatbots Exclusive

Quick Link

  • About
  • Contact
  • Privacy
Follow US

© 2008-23 SmartData Collective. All Rights Reserved.

Removed from reading list

Undo
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?