An incremental learning and integer-nonlinear programming approach to mining users’ unknown preferences for ubiquitous hotel recommendation

Tin-Chih Chen, Yi Chi Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

To further increase the successful recommendation rate of a ubiquitous hotel recommendation system, an incremental learning and integer-nonlinear programming approach (INLP) is proposed in this study to mine users’ unknown preferences. In the proposed methodology, an INLP problem is solved to adjust the values of weights in the recommendation mechanism to be closer to those in the decision-making mechanism so as to maximize the successful recommendation rate. In addition, the weights are adjusted on a rolling basis so that more historical data can be considered without inflating the INLP model. The experimental results showed that the proposed methodology outperformed several existing methods in increasing the successful recommendation rate, even with a cold start.

Original languageEnglish
Pages (from-to)2771-2780
Number of pages10
JournalJournal of Ambient Intelligence and Humanized Computing
Volume10
Issue number7
DOIs
StatePublished - 1 Jul 2019

Keywords

  • Incremental learning
  • Integer-nonlinear programming
  • Ubiquitous recommendation

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