1. Introduction

With the explosive growth of online information, such as news articles, email messages, scientific literature, government documents, and information about all kinds of products on the Web, search engines have now become essential tools in all aspects of our life; clearly, their effectiveness would directly affect our productivity and quality of life.

The current generation search engines are very useful, but they tend to work well only for easy queries (e.g., finding homepages or finding popular/known topics) and would perform poorly for difficult queries (e.g., queries with ambiguous words or queries about unknown topics). It is quite common that a user would have to reformulate a query many times, yet still would not get satisfactory results. In such cases, the user has to either spend a significant amount of time in searching or simply give up. Thus improving search accuracy for such difficult queries can bring significant benefits to users.

This project aims to develop an interactive search environment (called QueryClinic) to better support a user in finding such hard-to-find information. The main idea behind QueryClinic is to make the search process a collaborative process in which QueryClinic and a user would interact with each other and work together toward improving search results. This is in contrast with the current search process in which a search engine passively responds to a user's query with some search results. Specifically, QueryClinic would actively involve a user in the search process so that the user can give more input to the search process and also receive more guidance and assistance in reformulating queries. In this way, the system would be able to collect more informative feedback information and analyze the entire interaction session as a whole (rather than just a few words in the query) to understand more precisely the user's information need, which in turn helps the system better direct the search process and improve search accuracy.

2. Team members

• ChengXiang Zhai (Professor)
• Alexander Kotov (Ph.D. student)
• Yuanhua Lv (Ph.D. student)
• V.G.Vinod Vydiswaran (Ph.D. student)
• Xuanhui Wang (Ph.D. student)

• Negative feedback: When the initial search results are extremely poor, all the top-ranked documents may be non-relevant. To help users in such a situation, we studied how to learn from the top-ranked non-relevant documents (i.e., negative feedback) to improve the ranking of the unseen documents and proposed effective methods for negative feedback [Wang et al. SIGIR 08].

4. Selected Publications (See all publications)

---

Text Information Management Group Home | About| People | Projects| Publications | Downloads| Links| Wiki