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.

[1]  Vincent S. Tseng,et al.  Efficient mining and prediction of user behavior patterns in mobile web systems , 2006, Inf. Softw. Technol..

[2]  Maiga Chang,et al.  Recommend Touring Routes to Travelers According to Their Sequential Wandering Behaviours , 2009, 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks.

[3]  Duen-Ren Liu,et al.  A hybrid of sequential rules and collaborative filtering for product recommendation , 2009, Inf. Sci..

[4]  Özgür Ulusoy,et al.  A data mining approach for location prediction in mobile environments , 2005, Data Knowl. Eng..

[5]  Chih-Chieh Hung,et al.  A regression-based approach for mining user movement patterns from random sample data , 2011, Data Knowl. Eng..

[6]  Mojtaba Salehi,et al.  Hybrid recommendation approach for learning material based on sequential pattern of the accessed material and the learner's preference tree , 2013, Knowl. Based Syst..

[7]  Guangquan Zhang,et al.  BizSeeker: A hybrid semantic recommendation system for personalized government-to-business e-services , 2010, Internet Res..

[8]  Reda Alhajj,et al.  Effective web log mining and online navigational pattern prediction , 2013, Knowl. Based Syst..

[9]  Ismael Rivera,et al.  SPETA: Social pervasive e-Tourism advisor , 2009, Telematics Informatics.

[10]  Yoon Ho Cho,et al.  Mining changes in customer buying behavior for collaborative recommendations , 2005, Expert Syst. Appl..

[11]  Cindy Yoonjoung Heo,et al.  Application of revenue management practices to the theme park industry. , 2009 .

[12]  Analía Amandi,et al.  Building an expert travel agent as a software agent , 2009, Expert Syst. Appl..

[13]  Katerina Kabassi,et al.  Personalizing recommendations for tourists , 2010, Telematics Informatics.

[14]  Lora Aroyo,et al.  Semantics : Science , Services and Agents on the World Wide Web , 2008 .

[15]  Chieh-Yuan Tsai,et al.  A personalized route recommendation service for theme parks using RFID information and tourist behavior , 2012, Decis. Support Syst..

[16]  Yu-Chun Chen,et al.  A multi-stage collaborative filtering approach for mobile recommendation , 2009, ICUIMC '09.

[17]  Abolghasem Sadeghi-Niaraki,et al.  Ontology based personalized route planning system using a multi-criteria decision making approach , 2009, Expert Syst. Appl..

[18]  Ming-Tat Ko,et al.  Discovering time-interval sequential patterns in sequence databases , 2003, Expert Syst. Appl..

[19]  Ming-Syan Chen,et al.  Mining Mobile Sequential Patterns in a Mobile Commerce Environment , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[20]  Hua Lin,et al.  A hybrid fuzzy-based personalized recommender system for telecom products/services , 2013, Inf. Sci..