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A Deep Learning-Based Hotel Image Classifier for Online Travel Agencies

Year 2021, Volume: 23 Issue: 67, 257 - 264, 15.01.2021
https://doi.org/10.21205/deufmd.2021236722

Abstract

Along with the extensive application of information technologies, there has been increased usage of online travel agencies (OTA) to search holiday alternatives. Hotel images demonstrated by OTAs plays a critical role in providing information to ease selection process. Although serving the images in the right context is an important task to clearly reflect hotel properties, there has been no attempt in previous studies to organize hotel images into appropriate category. For this reason, we aim to conduct a study to organize and classify 20,000 hotel images using Convolutional Neural Networks (CNN), a prominent deep learning method widely applied in the field of computer vision. Due to the limited training data, we experiment transfer learning to train experimented models. In this phase, we choose a widely applied CNN models, VGG-16, VGG-19, and Inception-v3 which are trained on over one million images. The results demonstrate that experimented models achieve effective categorization of hotel images with the considerable accuracy scores. We believe that our study can help improve OTAs performance in competitive tourism market.

Thanks

Funding for this work was partially supported by the Research and Development Center of Tatilbudur.com accredited on Turkey - Ministry of Science.

References

  • Ling, L., Dong, Y., Guo, X., Liang, L. 2015. Availability management of hotel rooms under cooperation with online travel agencies, International Journal of Hospitality Management, vol. 50, pp. 145-152.
  • Hatton, M. 2004. Redefining the relationship: The future of travel agencies and the global agency contract in a changing distribution system, Journal of Vacation Marketing, vol. 10, no. 2, pp. 101-108.
  • Deng, J., Dong, W., Socher, R., Li, J., Li, K., Fei, L. 2009. ImageNet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, June, 248-255. IEEE.
  • Qassim, H., Verma, A., Feinzimer, D. 2018. Compressed residual-VGG16 CNN model for big data places image recognition. IEEE 8th Annual Computing and Communication Workshop and Conference, January, 169-175. IEEE.
  • MacKay, K. J., Couldwell, C. M. 2004. Using visitor-employed photography to investigate destination image. Journal of Travel Research, vol. 42, no. 4, pp. 390-396.
  • Phelps, A. 1986. Holiday destination image the problem of assessment: An example developed in Menorca. Tourism management, vol. 7, no. 3, pp. 168-180.
  • Zhang, S., Lee, D., Singh, P. V., Srinivasan, K. 2017. How much is an image worth? Airbnb property demand estimation leveraging large scale image analytics. Airbnb Property Demand Estimation Leveraging Large Scale Image Analytics.
  • Zhang, S. 2019. A structural analysis of sharing economy leveraging location and image analytics using deep learning. Carnegie Mellon University, PhD Thesis.
  • Simonyan, K., Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  • Pan, S.J., Yang, Q. 2009. A survey on transfer learning. IEEE Transactions on knowledge and data engineering, vol. 22, no. 10, pp. 1345-1359.
  • Bisong, E. 2019. Google Colaboratory. Building Machine Learning and Deep Learning models on Google Cloud platform, pp. 59-64. Apress, Berkeley, CA.

Çevrimiçi Seyahat Acenteleri için Derin Öğrenmeye Dayalı Otel Görüntülerinin Sınıflandırılması

Year 2021, Volume: 23 Issue: 67, 257 - 264, 15.01.2021
https://doi.org/10.21205/deufmd.2021236722

Abstract

Bilgi teknolojileri uygulamaların yaygınlaşması ile birlikte, tatil alternatifleri arayışında çevrimiçi seyahat acentelerinin kullanımı artmıştır. Çevrimiçi seyahat acenteleri tarafından sunulan otel görüntüleri, tatilcilere bilgi sağlayarak seçim sürecini kolaylaştırma aşamasında kritik bir rol oynamaktadır. Görüntülerin doğru bağlamda sunulması, otel özelliklerini açıkça yansıtmak için önemli bir işlem olduğu halde otel görüntülerini düzenleme ve uygun kategorilere yerleştirme girişiminde bulunan bir çalışmaya rastlanılmamaktadır. Bu sebeple, çalışmamıza bilgisayarlı görü alanında yaygın olarak uygulanan önemli bir derin öğrenme yöntemi olan Konvolüsyonel Sinir Ağları kullanılarak 20.000 otel görüntüsünü sınıflandıran bir yaklaşım gerçekleştirimi hedeflenmiştir. Sınırlı eğitim verileri nedeniyle, önerilen modelimizin eğitimi aşamasında transfer öğrenme yöntemi uygulanmıştır. Bu bağlamda, önerilen yaklaşımımız için bir milyondan fazla görüntü üzerinde eğitilmiş, yaygın olarak uygulanan CNN modelleri olan VGG-16, VGG-19 ve Inception-v3 tercih edilmiştir. Sonuçlar, test ettiğimiz modellerin otel görüntülerini göz ardı edilemeyecek doğruluk skoru ile etkin bir şekilde sınıflandırılmasını sağladığını göstermektedir. Çalışmamızın rekabetçi turizm pazarında çevrimiçi seyahat acentelerinin performansını artırmaya yardımcı olabileceğine inanmaktayız.

References

  • Ling, L., Dong, Y., Guo, X., Liang, L. 2015. Availability management of hotel rooms under cooperation with online travel agencies, International Journal of Hospitality Management, vol. 50, pp. 145-152.
  • Hatton, M. 2004. Redefining the relationship: The future of travel agencies and the global agency contract in a changing distribution system, Journal of Vacation Marketing, vol. 10, no. 2, pp. 101-108.
  • Deng, J., Dong, W., Socher, R., Li, J., Li, K., Fei, L. 2009. ImageNet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, June, 248-255. IEEE.
  • Qassim, H., Verma, A., Feinzimer, D. 2018. Compressed residual-VGG16 CNN model for big data places image recognition. IEEE 8th Annual Computing and Communication Workshop and Conference, January, 169-175. IEEE.
  • MacKay, K. J., Couldwell, C. M. 2004. Using visitor-employed photography to investigate destination image. Journal of Travel Research, vol. 42, no. 4, pp. 390-396.
  • Phelps, A. 1986. Holiday destination image the problem of assessment: An example developed in Menorca. Tourism management, vol. 7, no. 3, pp. 168-180.
  • Zhang, S., Lee, D., Singh, P. V., Srinivasan, K. 2017. How much is an image worth? Airbnb property demand estimation leveraging large scale image analytics. Airbnb Property Demand Estimation Leveraging Large Scale Image Analytics.
  • Zhang, S. 2019. A structural analysis of sharing economy leveraging location and image analytics using deep learning. Carnegie Mellon University, PhD Thesis.
  • Simonyan, K., Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  • Pan, S.J., Yang, Q. 2009. A survey on transfer learning. IEEE Transactions on knowledge and data engineering, vol. 22, no. 10, pp. 1345-1359.
  • Bisong, E. 2019. Google Colaboratory. Building Machine Learning and Deep Learning models on Google Cloud platform, pp. 59-64. Apress, Berkeley, CA.
There are 11 citations in total.

Details

Primary Language English
Journal Section Research Article
Authors

Fatma Bozyiğit 0000-0002-5898-7464

Alperen Taşkın 0000-0002-0388-736X

Kadir Akar This is me 0000-0002-3589-8303

Deniz Kılınç 0000-0002-2336-8831

Publication Date January 15, 2021
Published in Issue Year 2021 Volume: 23 Issue: 67

Cite

APA Bozyiğit, F., Taşkın, A., Akar, K., Kılınç, D. (2021). A Deep Learning-Based Hotel Image Classifier for Online Travel Agencies. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, 23(67), 257-264. https://doi.org/10.21205/deufmd.2021236722
AMA Bozyiğit F, Taşkın A, Akar K, Kılınç D. A Deep Learning-Based Hotel Image Classifier for Online Travel Agencies. DEUFMD. January 2021;23(67):257-264. doi:10.21205/deufmd.2021236722
Chicago Bozyiğit, Fatma, Alperen Taşkın, Kadir Akar, and Deniz Kılınç. “A Deep Learning-Based Hotel Image Classifier for Online Travel Agencies”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi 23, no. 67 (January 2021): 257-64. https://doi.org/10.21205/deufmd.2021236722.
EndNote Bozyiğit F, Taşkın A, Akar K, Kılınç D (January 1, 2021) A Deep Learning-Based Hotel Image Classifier for Online Travel Agencies. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 23 67 257–264.
IEEE F. Bozyiğit, A. Taşkın, K. Akar, and D. Kılınç, “A Deep Learning-Based Hotel Image Classifier for Online Travel Agencies”, DEUFMD, vol. 23, no. 67, pp. 257–264, 2021, doi: 10.21205/deufmd.2021236722.
ISNAD Bozyiğit, Fatma et al. “A Deep Learning-Based Hotel Image Classifier for Online Travel Agencies”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 23/67 (January 2021), 257-264. https://doi.org/10.21205/deufmd.2021236722.
JAMA Bozyiğit F, Taşkın A, Akar K, Kılınç D. A Deep Learning-Based Hotel Image Classifier for Online Travel Agencies. DEUFMD. 2021;23:257–264.
MLA Bozyiğit, Fatma et al. “A Deep Learning-Based Hotel Image Classifier for Online Travel Agencies”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, vol. 23, no. 67, 2021, pp. 257-64, doi:10.21205/deufmd.2021236722.
Vancouver Bozyiğit F, Taşkın A, Akar K, Kılınç D. A Deep Learning-Based Hotel Image Classifier for Online Travel Agencies. DEUFMD. 2021;23(67):257-64.

Dokuz Eylül Üniversitesi, Mühendislik Fakültesi Dekanlığı Tınaztepe Yerleşkesi, Adatepe Mah. Doğuş Cad. No: 207-I / 35390 Buca-İZMİR.