A Location-Item-Time sequential pattern mining algorithm for route recommendation

To survive in a rapidly changing environment, theme parks need to provide high quality services in terms of visitor tastes and preferences. Understanding the spatial and temporal behavior of visitors could enhance the attraction management and geographical distribution for visitors. To fulfill the need, this research defined a Location-Item-Time (LIT) sequence to describe visitor's spatial and temporal behavior. Then, the Location-Item-Time PrefixSpan (LIT-PrefixSpan) mining algorithm is developed to discover frequent LIT sequential patterns. Next, the route suggestion procedure is proposed to retrieve suitable LIT sequential patterns for visitors under the constraints of their intended-visiting time, favorite regions, and favorite recreation facilities. A simplified theme park is used as an example to show the feasibility of the proposed system. The experimental results show that the system can help managers understand visitors' behavior and provide appropriate visiting experiences for visitors. (C) 2014 Elsevier B.V. All rights reserved.