research-article

A-HA: a hybrid approach for hotel recommendation

Authors:

Huang Kung-Hsiang,

Lee Tzong-Hann,

Lin Shou-DeAuthors Info & Claims

RecSys Challenge '19: Proceedings of the Workshop on ACM Recommender Systems Challenge

Article No.: 4, Pages 1 - 5

https://doi.org/10.1145/3359555.3359560

Published: 20 September 2019 Publication History

Abstract

Session-based recommender system refers to a specific type of recommender system that focuses more on the transactional structure of each session rather than the user and item interactions [16]. It is stated that the users' interactions are mostly homogeneous in the same sessions, while being heterogeneous across different sessions [5]. Therefore, it is essential to extract the interest dynamics of users within each session. The 2019 ACM Recsys Challenge [10] aims to apply session-based recommender systems to the domain of travel metasearch. The goal is to predict which hotels are clicked in the search results based on the context of each session. In this paper, we propose our approach to effectively tackle the challenge. It involves an ensemble of three models, LightGBM, XGBoost, and a Neural Network based on DeepFM [6] that is capable of handling sequential features. Our team, RosettaAI, won the 4th place in this challenge, scoring 0.679933 on the final leaderboard. The source code is available online ¹.

References

[1]

Larry C Andrews and Larry C Andrews. 1992. Special functions of mathematics for engineers. McGraw-Hill New York.

[2]

Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). ACM, New York, NY, USA, 785--794.

Digital Library

[3]

Junyoung Chung, Çaglar Gülçehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. CoRR abs/1412.3555 (2014). arXiv:1412.3555 http://arxiv.org/abs/1412.3555

[4]

Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems. ACM, 191--198.

Digital Library

[5]

Yufei Feng, Fuyu Lv, Weichen Shen, Menghan Wang, Fei Sun, Yu Zhu, and Keping Yang. 2019. Deep Session Interest Network for Click-Through Rate Prediction. arXiv preprint arXiv.1905.06482 (2019).

[6]

Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017).

[7]

Ya-Han Hu, Pei-Ju Lee, Kuanchin Chen, J Michael Tarn, and Duyen-Vi Dang. 2016. Hotel Recommendation System based on Review and Context Information: a Collaborative Filtering Appro. In PACIS. 221.

[8]

Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neurai information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 3146--3154. http://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf

[9]

Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv 1412.6980 (2014).

[10]

Peter Knees, Yashar Deldjoo, Farshad Bakhshandegan Moghaddam, Jens Adamczak, Gerard-Paul Leyson, and Philipp Monreal. 2019. RecSys Challenge 2019: Session-based Hotel Recommendations. In Proceedings of the Thirteenth ACM Conference on Recommender Systems (RecSys '19). ACM, New York, NY, USA, 2.

Digital Library

[11]

Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017. Automatic differentiation in PyTorch. (2017).

[12]

Dragomir R Radev, Hong Qi, Harris Wu, and Weiguo Fan. 2002. Evaluating Web-based Question Answering Systems. In LREC.

[13]

Badrul Munir Sarwar, George Karypis, Joseph A Konstan, John Riedl, et al. 2001. Item-based collaborative filtering recommendation algorithms. WWW 1 (2001), 285--295.

Digital Library

[14]

Qusai Shambour, Mouath Hourani, and Salam Fraihat. 2016. An item-based multi-criteria collaborative filtering algorithm for personalized recommender systems. International Journal of Advanced Computer Science and Applications 7, 8 (2016), 274--279.

[15]

Tri Duc Nguyen Tang. 2019. Knowledge Distillation with NN + RankGauss. Retrieved June 5, 2019 from https://www.kaggle.com/mathormad/knowledge-distillation-with-nn-rankgauss/data

[16]

Shoujin Wang, Longbing Cao, and Yan Wang. 2019. A survey on session-based recommender systems. arXiv preprint arXiv:1902.04864 (2019).

Cited By

Jannach Dde Souza P. Moreira GOldridge E(2020)Why Are Deep Learning Models Not Consistently Winning Recommender Systems Competitions Yet?Proceedings of the Recommender Systems Challenge 202010.1145/3415959.3416001(44-49)Online publication date: 26-Sep-2020
https://dl.acm.org/doi/10.1145/3415959.3416001
Adamczak JDeldjoo YMoghaddam FKnees PLeyson GMonreal P(2020)Session-based Hotel Recommendations DatasetACM Transactions on Intelligent Systems and Technology10.1145/341237912:1(1-20)Online publication date: 13-Nov-2020
https://dl.acm.org/doi/10.1145/3412379
Ngaffo AAyeb WChoukair Z(2020)A Bayesian Inference Based Hybrid Recommender SystemIEEE Access10.1109/ACCESS.2020.29988248(101682-101701)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2998824

Index Terms

A-HA: a hybrid approach for hotel recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Session-based Hotel Recommendations Dataset: As part of the ACM Recommender System Challenge 2019
Regular Papers

In 2019, the Recommender Systems Challenge [17] dealt for the first time with a real-world task from the area of e-tourism, namely the recommendation of hotels in booking sessions. In this context, we present the release of a new dataset that we believe ...
Session-based recommendations with sequential context using attention-driven LSTM
Abstract
A Session-based recommender system (SBRS) captures the dynamic behavior of a user to provide recommendations for the next item in the current session. On providing the user’s past interactions of ongoing sessions, the SBRS predicts the next item ...
A comprehensive mechanism for hotel recommendation to achieve personalized search engine

Search engines are as important as recommender systems for hotel selections. However, the recommended lists of search engines are usually non-personalized and low accuracy. In order to deal with these issues in search engines, a comprehensive mechanism ...

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Information & Contributors

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Published In

cover image ACM Other conferences

RecSys Challenge '19: Proceedings of the Workshop on ACM Recommender Systems Challenge

September 2019

49 pages

ISBN:9781450376679

DOI:10.1145/3359555

Program Chairs:
Peter Knees
TU Wien, Austria
,
Yashar Deldjoo
Polytechnic University of Bari, Italy
,
Farshad Bakhshandegan Moghaddam
Karlsruhe Institute ofTechnology, Germany
,
Jens Adamczak
trivago N.V., Germany
,
Gerard Leyson
trivago N.V., Germany
,
Philipp Monreal
trivago N.V., Germany

Copyright © 2019 ACM.

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New York, NY, United States

Publication History

Published: 20 September 2019

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RecSys Challenge '19

RecSys Challenge '19: ACM Recommender Systems Challenge 2019 Workshop

September 20, 2019

Copenhagen, Denmark

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Cited By

Jannach Dde Souza P. Moreira GOldridge E(2020)Why Are Deep Learning Models Not Consistently Winning Recommender Systems Competitions Yet?Proceedings of the Recommender Systems Challenge 202010.1145/3415959.3416001(44-49)Online publication date: 26-Sep-2020
https://dl.acm.org/doi/10.1145/3415959.3416001
Adamczak JDeldjoo YMoghaddam FKnees PLeyson GMonreal P(2020)Session-based Hotel Recommendations DatasetACM Transactions on Intelligent Systems and Technology10.1145/341237912:1(1-20)Online publication date: 13-Nov-2020
https://dl.acm.org/doi/10.1145/3412379
Ngaffo AAyeb WChoukair Z(2020)A Bayesian Inference Based Hybrid Recommender SystemIEEE Access10.1109/ACCESS.2020.29988248(101682-101701)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2998824

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