Personalized travel package recommendation

Qi Liu, Yong Ge, Zhongmou Li, Enhong Chen, Hui Xiong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

155 Scopus citations


As the worlds of commerce, entertainment, travel, and Internet technology become more inextricably linked, new types of business data become available for creative use and formal analysis. Indeed, this paper provides a study of exploiting online travel information for personalized travel package recommendation. A critical challenge along this line is to address the unique characteristics of travel data, which distinguish travel packages from traditional items for recommendation. To this end, we first analyze the characteristics of the travel packages and develop a Tourist-Area-Season Topic (TAST) model, which can extract the topics conditioned on both the tourists and the intrinsic features (i.e. locations, travel seasons) of the landscapes. Based on this TAST model, we propose a cocktail approach on personalized travel package recommendation. Finally, we evaluate the TAST model and the cocktail approach on real-world travel package data. The experimental results show that the TAST model can effectively capture the unique characteristics of the travel data and the cocktail approach is thus much more effective than traditional recommendation methods for travel package recommendation.

Original languageEnglish (US)
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining, ICDM 2011
Number of pages10
StatePublished - 2011
Externally publishedYes
Event11th IEEE International Conference on Data Mining, ICDM 2011 - Vancouver, BC, Canada
Duration: Dec 11 2011Dec 14 2011

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Conference11th IEEE International Conference on Data Mining, ICDM 2011
CityVancouver, BC

ASJC Scopus subject areas

  • Engineering(all)


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