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Jian-Tao Sun's Selected Projects

Intent Engine

Users rely on the Web to complete many tasks online, e.g., business travel, product research and even planning an entertainment activity. Usualy users need to interact with various services and software, such as browsing, search, and social networks, to access different kinds of information, to make comparisons and to have conversations with friends. In order to make the process easy and efficient, it is important to understand what user wants, what information needs can be satisfied by websites / service providers, and how to enable natural conversations between users and systems to have tasks completed.

Intent engine plays an important role in delivering great end user experience. In this project, we study various research problems, including intent representation, intent organization, algorithms to map user inputs to intent, intent prediction and how to connect users with different services. Particularly, we study how to scale up the intent engine, i.e., to enable intelligent task completion experiences for various task domains (shopping, travel, local, etc.). Furthermore, based on the research work, we are proposing new product features and even new products and evaluate user experiences on different types of computing devices.

Opinion Mining

Opinion data is widely spread on the Web, in various forms like product reviews, blogs and microblogs, etc. Since in many cases, user opinion is expressed in text format, it is not easy to digest/aggregate them efficiently. This project targets to organize opinion data and facilitate the usage of them. The following research problems are studied in this project:

  • Sentiment classification
  • Opinion topic mining
  • Opinion summarization
  • Cross-domain sentiment classification
  • Opinion word extraction
  • Comparative sentiment classification

Research algorithms of this project are also used by Microsoft Bing products.

Comparative Web Search
In this project, we propose a novel search problem: Comparative Web Search (CWS). The task of CWS is to seek relevant and comparative information from the Web to help users conduct comparisons among a set of topics. A system called CWS is developed to effectively facilitate Web users' comparison needs. We propose a novel interface which supports two types of view modes: a pair-view which displays the result in the page level, and a cluster-view which organizes the comparative pages into the themes and displays the extracted phrases to facilitate users' comparison.

Comparison Shopping
We do research work to facilitate users’ online shopping experience. A graph based search interface is implemented for users to specify their requirements on each aspect of the product. The products are ranked in the subjective space based on mining user opinion data. The graph based interface is also very easy for making comparisons between different products.

Categorization Engine
The goal of this research is to automatically classify search queries and Web pages into some predefined categories. A series of algorithms have been proposed and implemented in this project.

Usage Data Mining
In this project, we conduct research on usage log data to extract user intelligence and benefit businesses like Web search and online advertising.