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Multiple Days Trip Recommendation Based on Check-in Data

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Abstract

A travel recommender system can generate suggested itineraries for users based on their preferences. However, current systems are not capable of simultaneously considering trip length, distance, user requirements and preferences when making recommendations, being only equipped to consider one or two of these variables at one time. Also, to generate recommendations the system must process all attractions in the database, requiring more data access and longer processing time. We analyzed the check-in records of users and utilized a new concept of time intervals combined with a multiple days trip algorithm to produce itineraries compatible with the interests and needs of users. By applying R-tree to the travel recommender system, we reduced data access times and computation time. Lastly, we propose a trip evaluator equation that can be used to compare the strengths and weaknesses of each algorithm. Experimental results verified the effectiveness of our method.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, 701, Taiwan, R.O.C.

    Heng-Ching Liao & Chiang Lee

  2. Department of Information Engineering and Computer Science, Feng Chia University, Taichung, 407, Taiwan, R.O.C.

    Yi-Chung Chen

Authors
  1. Heng-Ching Liao

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  2. Yi-Chung Chen

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  3. Chiang Lee

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Corresponding author

Correspondence toYi-Chung Chen.

Editor information

Editors and Affiliations

  1. National University of Kaohsiung, Kaohsiung City, Taiwan

    Leon Wang

  2. Matsuyama University, Matsuyama, Japan

    Shiro Uesugi

  3. National University of Kaohsiung, Kaohsiung City, Taiwan

    I-Hsien Ting

  4. Osaka University, Osaka, Japan

    Koji Okuhara

  5. National University of Kaohsiung, Kaohsiung City, Taiwan

    Kai Wang

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© 2015 Springer-Verlag Berlin Heidelberg

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Liao, HC., Chen, YC., Lee, C. (2015). Multiple Days Trip Recommendation Based on Check-in Data. In: Wang, L., Uesugi, S., Ting, IH., Okuhara, K., Wang, K. (eds) Multidisciplinary Social Networks Research. MISNC 2015. Communications in Computer and Information Science, vol 540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48319-0_25

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eBook
JPY 5719
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
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Softcover Book
JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only


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