In event-based social networks, user preferences mined from the influences of geographical locations, event categories, and social and temporal preferences have been exploited for event recommendations by assuming that each of these influences has the same weight for all users. However, in the reality, a user would have different degrees of importance for these influences on deciding whether to participate in an event. In this paper, we propose a personalized event recommendation framework called SoCaST*, which employs the multi-criteria decision making approach to rank events. In SoCaST*, preference models are built to compute geographical, categorical, social, and temporal influences, and a personalized weight is estimated for each criterion (i.e., each influence). By utilizing the personalized criterion’s weight, dominance intensity measures (i.e., dominating and dominated measures) are computed for alternatives (i.e., candidate events) of each criterion, and the set of alternatives is ranked based on the estimated dominance intensity measures to recommend k top-ranked events. Extensive experiments are conducted based on two large real-world data sets collected from Meetup.com to evaluate the performance of SoCaST*. Experimental results show that SoCaST* performs better than the state-of-the-art techniques designed for event recommendations.
Note from Journals.Today : This content has been auto-generated from a syndicated feed.