1. Model Personalization (1) : Data Fusion Improve frame and answer (of persistent query) generation through Data Fusion (local fusion on personal and topical level) and Interactive Relevancy Feedback. At stage 1, we have successfully integrated effective data fusion into HITIQA to optimize the successful rate of useful paragraph extraction. At stage 2, the emphasis will be on using user judgments at different times to adjust fusion parameters chronologically, with a time- sensitive weighting scheme, to fit the evolving understanding of the same user on the topic.

2. Model Personalization (2) : Document Qualities Judgment Personalization of automatic document qualities assessment algorithm, through advanced statistical analysis and machine learning, to identify (1) global qualities predictors, (2) general formal model of qualities assessment, and (3) personal weight on parameters for individual preference. At stage1, we have established a few models in estimation of various document qualities, based on textual features and linguistic patterns of a document, with successful rate much better than chance, on a global level. At stage 2, we will continue our previous endeavor in identification of good predictive variables of qualities, with a new emphasis on a local level: to mimic the personal mental model of a user.

3. Model Personalization (3): Integration through Experiment We will integrate the previous two personalization and other desired mechanisms into a single interface, by converting related functionalities into position and iconic information in the user display. At stage 2, focusing on same user and persistent query, we will investigate the impacts of Interface Options on analyst satisfaction and identify the best combination strategy, and to establish the effectiveness measure on a personal level. In addition to ANOVA and multiple comparisons such as Tukey’s method, we will use orthogonal arrays method to reduce the number of experimental configuration to be studied. Instead of trying to identify the causes of negative effect, we will focus on how to neutralizes negative effect to obtain a higher quality result with fewer experiments.