19/10/2020

IDEAL: IDEntifying the user’s IdeAL tuple via sorting in the database

Chen Chen, Jiping Zheng, Weijun Yan, Meijing Wang

Keywords: sorting, k-regret query, skyline query

Abstract Paper Similar Papers

Abstract: Beyond top-k and skyline queries, in the last decade regret minimization queries have played an important role in the database community to solve multi-criteria decision making problems. To reduce the user’s regret, interaction is proved to be an efficient way to turn the user’s regret ratio to 0, i.e. identifying the user’s ideal point from the large dataset. However, existing interactive regret minimization framework needs more rounds of user’s interaction to identify her/his ideal point. To reduce the number of interaction rounds, we propose a system, called IDEAL, IDE ntifying the user’s ideAL tuple via sorting in the database. In our system, we use the National Basketball Association (NBA) dataset to show our interactive framework via sorting mechanism, which can make the regret minimization query quickly converge to the user’s ideal data point from initial displayed several points with few rounds of the user’s interaction.

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