As two important features of hesitant fuzzy linguistic term sets (HFLTSs), distance and similarity measures have been applied widely in many fields such as pattern recognition, decision making and prediction. Through analyzing the existing distance and similarity measures on HFLTSs, we find that they are not reasonable in some cases. Therefore, we first define the hesitance degree on HFLTSs to reflect the hesitant degree among several linguistic terms. On the basis of hesitance degree on HFLTSs, we develop several novel distance measures and further discuss their properties. Afterwards, several similarity measures based on hesitance degree are proposed and applied to pattern recognition. By comparing our novel proposed distance and similarity measures with the existing methods and giving an example of pattern recognition, we prove that our proposed distance and similarity measures are more reliable than the previous method in some cases.

A lot of research work has studied the auction mechanism of uncertain advertising cooperation between the e-commerce platform and advertisers, but little has focused on pricing strategy in stable advertising cooperation under a certain market power structure. To fill this gap, this paper makes a study of the deep interest distribution of two parties in such cooperation. We propose a pricing strategy by building two stackelberg master-slave models when the e-commerce platform and the advertiser are respectively the leader in the cooperation. It is analyzed that the optimization solution of the profits of both parties and the total system are affected by some main decision factors including the income commission proportion, the advertising product price and the cost of advertising effort of both parties’ brand in different dominant models. Then, some numerical studies are used to verify the effectiveness of the models. Finally, we draw a conclusion and make some suggestions to the platforms and the advertisers in the e-commerce advertising cooperation.