Identifying Prospective Visitors and Recommending Personalized Booths in the Exhibition Industry

Exhibition industry is important business domains to many countries. Not only lots of countries designated the exhibition industry as tools to stimulate national economics, but also many companies offer millions of service or products to customers. Recommender systems can help visitors navigate through large information spaces of various booths. However, no study before has proposed a methodology for identifying and acquiring prospective visitors although it is important to acquire them. Accordingly, we propose a methodology for identifying, acquiring prospective visitors, and recommending the adequate booth information to their preferences in the exhibition industry. We assume that a visitor will be interested in an exhibition within same class of exhibition taxonomy as exhibition which the visitor already saw. Moreover, we use user-based collaborative filtering in order to recommend personalized booths before exhibition. A prototype recommender system is implemented to evaluate the proposed methodology. Our experiments show that the proposed methodology is better than the item-based CF and have an effect on the choice of exhibition or exhibit booth through automation of word-of-mouth communication.