Grant: Discovery Grants Program - Individual

Information retrieval (IR) systems including various search engines have played important roles in our life. They include general web search engines (such as Google), vertical search engines (such as product search in Amazon.com), specific media search (such as image search), and so on. In the past years I have made fruitful and significant contributions in several important areas of machine learning. The objectives of this research program are to expand my research in machine learning, and leverage it to improve the performance and user experience of IR systems for people to easily obtain highly accurate and useful information. One important question we will study in this research program is how hierarchies can be used to effectively improve search accuracy and user experience. Hierarchies or taxonomies on topics, genres, media types, and so on, are natural to human and important in organizing human knowledge. It would be ideal if keyword search and hierarchical browsing (roll-up and drill-down) can be seamlessly integrated. For example, searching ""china"" in the art category will remove ambiguity of china as the country. However, this has not been done in web search engines (such as Google), because billions of webpages must be classified into hierarchies by machine learning (i.e., not by human as in DMOZ, the Open Directory Project). Other fascinating research challenges include: how can active learning with generalized queries (our work) improve relevance feedback in IR? How can we utilize many ""cheap"" labellers (such as Amazon Mechanical Turk) in active learning of hierarchies? How can we learn from behaviours and cognitive models of users of IR systems? The outcome and impact of our research program will be fruitful and significant. First of all, it will greatly advance both machine learning and IR research. We will continue to publish top-rated conference and journal papers. Second, we will build prototypes of advanced IR systems and search engines with machine learning techniques. Third, we will improve usability of our IR prototypes in large-scale user studies. Our ultimate objective of this research is to make it easy for people to obtain highly accurate and useful information.