INLP-BPN approach for recommending hotels to a mobile traveler

Tin-Chih Chen, Chi Wei Lin*

*Corresponding author for this work

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Existing systems for recommending hotels to mobile travelers are subject to several problems. For example, a traveler might choose a dominated hotel that is inferior to another hotel in all aspects. This problem cannot be solved by simply changing the weights assigned to the attributes of a hotel. In addition, a nonlinear recommendation mechanism, instead of a linear one, may be more effective for tailoring the recommendation result to a traveler’s choice. To address these concerns, this study applied two treatments. First, an artificial attribute is added to each hotel to model a traveler’s unknown preference for that hotel. The value of a traveler’s unknown preference is determined by solving an integer nonlinear programming problem. Subsequently, a backward propagation network is constructed to map the recommendation results to travelers’ choices, to improve the successful recommendation rate. The effectiveness of the proposed methodology was evaluated in a field study conducted in a small region of Seatwen District, Taichung City, Taiwan, and the experimental results supported its superiority over several existing methods in improving the successful recommendation rate.

Original languageEnglish
Pages (from-to)329-336
Number of pages8
JournalJournal of Ambient Intelligence and Humanized Computing
Volume9
Issue number2
DOIs
StatePublished - 1 Apr 2018

Keywords

  • Backward propagation network
  • Hotel
  • Integer nonlinear programming
  • Mobile
  • Recommendation

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