Personalized trip recommendation with multiple constraints by mining user check-in behaviors

  • Authors:

  • Eric Hsueh-Chan Lu;Ching-Yu Chen;Vincent S. Tseng

  • Affiliations:

  • National Cheng Kung University, Tainan City, Taiwan (R.O.C.);National Cheng Kung University, Tainan City, Taiwan (R.O.C.);National Cheng Kung University, Tainan City, Taiwan (R.O.C.)

  • Venue:
  • Proceedings of the 20th International Conference on Advances in Geographic Information Systems
  • Year:
  • 2012

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Abstract

In recent years, researches on travel recommendation have attracted extensive attentions due to the wide applications. Among them, one of the active topics is constraint-based trip recommendation for meeting user's personal requirements. Although a number of studies on this topic have been proposed in literatures, most of them only regard the user-specific constraints as some filtering conditions for planning the trip. In fact, immersing the constraints into travel recommendation systems to provide a personalized trip is desired for users. Furthermore, time complexity of trip planning from a set of attractions is sensitive to the scalability of travel regions. Hence, how to reduce the computational cost by parallel cloud computing techniques is also a critical issue. In this paper, we propose a novel framework named Personalized Trip Recommendation (PTR) to efficiently recommend the personalized trips meeting multiple constraints of users by mining user's check-in behaviors. In PTR, a mining-based module is first proposed to estimate the scores of attractions by considering both of user-based preferences and temporal-based properties. Then, a trip planning algorithm named Parallel Trip-Mine+ is proposed to efficiently plan the trip that satisfies multiple user-specific constraints. To our best knowledge, this is the first work on travel recommendation that considers the issues of multiple constraints, social relationship, temporal property and parallel computing simultaneously. Through comprehensive experimental evaluations on a real check-in dataset obtained from Gowalla, PTR is shown to deliver excellent performance.