An introduction to prognostic search

Abstract

Implicit relevance feedback has received wide attention recently, as a means to capture the search context in improving search accuracy. However, implicit feedback is usually not available to public or even research communities at large for reasons like being a potential threat to privacy of web users. This makes it difficult to experiment and evaluate web search related research and especially web search personalization algorithms. Given these problems, we are motivated towards an artificial way of creating user relevance feedback, based on insights from query log analysis. We call this simulated feedback. We believe that simulated feedback can be immensely beneficial to web search engine and personalization research communities by greatly reducing efforts involved in collecting user feedback. The benefits from –Simulated feedback— are-It is easy to obtain and also the process of obtaining the feedback data is repeatable and customizable. In this chapter, we describe a simple yet effective approach for creating simulated feedback. We evaluated our system using clickthrough data of a set of real world users and achieved 65% accuracy in generating click-through data of those users.