Abstract
Recommendation Systems at OYO solve a complex personalization problem with scale and sophistication. The authors have focused on the development of the best-in-class recommendation system in the hospitality industry using a deep learning based model. The objective of the work is to develop a recommendation model which uses sequences of user interactions with contents derived from user interactions. The hybrid model described in the paper is a deep recurrent neural network based architecture split into two components: first, an embedding generation model and then a deep prediction and ranking model. The models have shown significant performance improvement both online and offline over existing collaborative filtering based models across geographies irrespective of traffic density and hotel supply density. The success of the deep learning based hybrid recommendation model at OYO across different geographies indicates immense potential of such recommender systems in industries such as travel, hospitality etc.
Keywords
- Neural network
- Deep learning
- RNN
- GRU
- LSTM
- BiLSTM
- Graph-based model
- CTR
- Conversion
- Realization
- CxR
- Embeddings
- Hit Ratio
- Mean reciprocal rank
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Adomavicius, G., Tuzhilin, : Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Covington, P., Adams, J., Sargin, E.: Deep neural networks for youtube recommendations. In: Recsys, pp. 191–198 (2016)
Gomez-Uribe, C.A., Hunt, N.: The Netflix recommender system: algorithms, business value, and innovation. TMIS 6(4), 13 (2016)
James Davidson, Benjamin Liebald, Junning Liu, Palash Nandy, Taylor Van Vleet, Ullas Gargi, Sujoy Gupta, Yu He, Mike Lambert, Blake Livingston, and Dasarathi Sampath. 2010. The YouTube Video Recommendation System. In Recsys
Cheng, H.-T., et al.: Wide & deep learning for recommender systems. In: Recsys, pp. 7–10 (2016)
Okura, S., Tagami, Y., Ono, S., Tajima, A.: Embedding-based news recommendation for millions of users. In: SIGKDD (2017)
Xiaoyuan, S., Khoshgoaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4 (2009)
Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adapt. Interact. 12(4), 331–370 (2002)
Fernandez-Tobıas, I., Cantador, I., Kaminskas, M., Ricci, F.: Cross-domain recommender systems: a survey of the state of the art. In: Spanish Conference on Information Retrieval, 24 (2012)
Khan, M.M., Ibrahim, R., Ghani, I.: Cross domain recommender systems: a systematic literature review. ACM Comput. Surv. 50, 3 (2017)
Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender systems: An Introduction (2010)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: WWW, pp. 173–182 (2017)
Koren, Y., Bell, R.: Advances in collaborative filtering. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 77–118. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_3
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Proceedings of NIPS, pp. 1257–1264. Curran Associates Inc., USA (2007)
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Computation 9, 8 (Nov. 1997), 1735–1780
Cho, K., et al.: Learning phrase representations using RNN encoder–decoder for statistical machine translation. In: Proceedings of EMNLP. Association for Computational Linguistics, pp. 1724–1734 (2014)
Donkers, B.L., Ziegler, J.: Sequential user-based recurrent neural network recommendations. In: Proceedings of RecSys, pp. 152–160. ACM, New York, NY, USA (2017)
Hidasi, B., Karatzoglou, A.: Recurrent neural networks with top-k gains for session-based recommendations. In: Proceedings of CIKM, pp. 843–852. ACM, New York, NY, USA (2018)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: Proceedings of ICLR (2016)
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Proceedings of CIKM, pp. 1419–1428. ACM, New York, NY, USA (2017)
Quadrana, M., Karatzoglou, A., Hidasi, B., Cremonesi, P.: Personalizing session-based recommendations with hierarchical recurrent neural networks. In: Proceedings of RecSys, pp. 130–137. ACM, New York, NY, USA (2017)
Wu, C.-Y., Ahmed, A., Beutel, A., Smola, A.J. Jing, H.: recurrent recommender networks. In: Proceedings of WSDM, pp. 495–503. ACM, New York, NY, USA (2017)
Yu, F., Liu, Q., Wu, S., Wang, L., Tan, T.: A dynamic recurrent model for next basket recommendation. In: Proceedings of SIGIR, pp. 729–732. ACM, New York, NY, USA (2016)
Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)
Grbovic, C.: Real-time personalization using embeddings for search ranking at airbnb. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 311–320. KDD (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 IFIP International Federation for Information Processing
About this paper
Cite this paper
Rankawat, A., Kumar, R., Kumar, A. (2022). Deep Recurrent Neural Networks for OYO Hotels Recommendation. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 646. Springer, Cham. https://doi.org/10.1007/978-3-031-08333-4_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-08333-4_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-08332-7
Online ISBN: 978-3-031-08333-4
eBook Packages: Computer ScienceComputer Science (R0)
-
Published in cooperation with
http://www.ifip.org/
