Enabling soft queries for data retrieval

Data retrieval finding relevant data from large databases - has become a serious problem as myriad databases have been brought online in the Web. For instance, querying the for-sale houses in Chicago from realtor.com returns thousands of matching houses. Similarly, querying "digital camera" in froogle.com returns hundreds of thousand of results. This data retrieval is essentially an online ranking problem, i.e., ranking data results according to the user's preference effectively and efficiently. This paper proposes a new rank query framework, for effectively incorporating "user-friendly" rank-query formulation into "data base (DB)-friendly" rank-query processing, in order to enable "soft" queries on databases. Our framework assumes, as the "back-end," the score-based ranking model for expressive and efficient query processing. On top of the score-based model, as the "front-end," we adopt an SVM-ranking mechanism for providing intuitive and exploratory query formulation. In essence, our framework enables users to formulate queries simply by ordering some sample objects, while learning the "DB-friendly" ranking function F from the partial orders. Such learned functions can then be processed and optimized by existing database systems. We demonstrate the efficiency and effectiveness of our framework using real-life user queries and datasets: our results show that the system effectively learns quantitative ranking functions from qualitative feedback from users with efficient online processing.