Paper: Generating Simulated Relevance Feedback: A Prognostic Search approach

ACL ID C10-2068
Title Generating Simulated Relevance Feedback: A Prognostic Search approach
Venue International Conference on Computational Linguistics
Session Poster Session
Year 2010
Authors

Implicit relevance feedback has proved to be a important resource in improv- ing search accuracy and personalization. However, researchers who rely on feed- back data for testing their algorithms or other personalization related problems are loomed with problems like unavailabil- ity of data, staling up of data and so on. Given these problems, we are mo- tivated towards creating a synthetic user relevance feedback data, based on insights from query log analysis. We call this sim- ulated feedback. We believe that simu- lated feedback can be immensely benefi- cial to web search engine and personaliza- tion research communities by greatly re- ducing efforts involved in collecting user feedback. The benefits from ”Simulated feedback” are - it is easy to obtain and also the process of obtai...