An Interval Valued Hesitant Fuzzy Clustering Approach for Location Clustering and Customer Segmentation

Because of the irrepressible growth in information technologies and telecommunication infrastructure especially for mobile devices, people are more disposed to search proper products and find out attractive offers with lower prices. In order to reach potential customers, companies deal with offering personalized messages including special promotions and discounts. In this respect, recommender systems have begun to use as one of the essential tools for making appropriate selections considering diversified conditions and personal preferences. On the other hand, users’ preferences could not be easily determined or predicted in some cases, as seen in visiting prediction of mobile users. Thus, the use of location based service applications enable the determination of users visiting patterns, except making predictions. In this study, an interval valued hesitant fuzzy clustering approach is adapted based on location similarity and fuzzy c means clustering is applied for user segmentation. After that, matching location groups and user segments is provided the representation of user visiting tendency. By using this approach, advertisers will be able to handle their advertisements considering location similarities and user groups that helps the implementation of personalized advertising recommender systems.