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Research Article

Smart recommendation for tourist hotels based on multidimensional information: a deep neural network model

, , , , ORCID Icon &
Article: 1959651 | Received 27 Jun 2020, Accepted 20 Jul 2021, Published online: 02 Aug 2021
 

ABSTRACT

Most hotel recommendation systems currently rely on text-based information or meta-data. We develop a deep network recommendation model with three modalities – picture, review, and scoring .We propose a unifified deep neural network including an embedding layer, pooling layer, and fully connected layer. Comparing with other algorithms, we verify its efficacy in improving travel recommendations based on the hotel data crawled from Ctrip and the major evaluation indicators. Our study contributes to the literature by building a knowledge model for tourist hotels based on the analysis of user-generated data and providing practical guidance for hotel managers and users.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research has been supported by the National Natural Science Foundation of China: (1)NSFC-71871172, Model of Risk knowledge acquisition and Platform governance in FinTech based on deep learning, and (2) NSFC-71571139, Outlier Analytics and Model of Outlier Knowledge Management in the context of Big Data.

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