Top-k Algorithm for User Preferences based on Selection Strategy
Jinling Song, Guohua Liu, Haibin Liu, Huang Li-ming, Wu Yun-long · International Journal of Database Theory and Application · 2016
In order to deal with multiple user preferences and improve query efficiency, selection strategy is adopted for top-k query to depress the compare operations.Firstly, the kth order statistics are selected randomly along with partitioning the data set basing on it, and the top-k result set can be received after several recursive partitions.Secondly, to select the kth order statistics accurately, the approximate kth order statistics is choose as threshold according to the similarity of user preference and system preference, and the top-k query result set can be accessed through simple comparison.Finally, the time complexities of presented algorithms are analyzed and their correctness and completeness are proved respectively.The experimental results show that our algorithms improve the efficiency of top-k query greatly.