A nonlinear semantic model for selecting association rules for users

A lot of papers have studied how to select accurate association rules for the users, and most of them make use of efficient statistical measurements, such as support, confidence, chi-square, lift, collective strength, and so on. However, it is still an open question about how to select a more proper statistical measurement for a certain user to find association rules in a certain domain. Since different measurements consider different interesting aspects of the association rules, we propose a novel method, named RuleRank, combining these statistical methods, in order to provide an alternative with more flexibility and robustness for users. In order to capture the userpsilas interest more closely, we also integrate semantic similarity into RuleRank model. The simulation results show that RuleRank model could give satisfying results for the user.