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Boosting algorithms for a session-based, context-aware recommender system in an online travel domain

Published: 20 September 2019 Publication History

Abstract

To keep up with a highly competitive the online hotel booking sector, it is necessary to develop fast and robust recommender systems. The 2019 RecSys Challenge is focused on ways we may use session-based and context-aware signals from users to improve the quality of hotel booking recommendations. In this paper, we present our approach to the challenge. We focus on the proper problem representation, feature extraction, and model blending. Our team achieved the 1st place out of 500 teams in the challenge, with the final MRR score of 0.685711.

References

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Cited By

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  • (2024)A Systematic Literature Review on AI-Based Recommendation Systems and Their Ethical ConsiderationsIEEE Access10.1109/ACCESS.2024.345105412(121223-121241)Online publication date: 2024
  • (2024)A systematic literature review of recent advances on context-aware recommender systemsArtificial Intelligence Review10.1007/s10462-024-10939-458:1Online publication date: 16-Nov-2024
  • (2024)Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender SystemsAdvances in Information Retrieval10.1007/978-3-031-56027-9_9(140-156)Online publication date: 24-Mar-2024
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  1. Boosting algorithms for a session-based, context-aware recommender system in an online travel domain

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    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
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    New York, NY, United States

    Publication History

    Published: 20 September 2019

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    Author Tags

    1. RecSys challenge
    2. boosting
    3. feature engineering
    4. mean reciprocal rank

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    RecSys Challenge '19

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    Overall Acceptance Rate 11 of 15 submissions, 73%

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    Cited By

    View all
    • (2024)A Systematic Literature Review on AI-Based Recommendation Systems and Their Ethical ConsiderationsIEEE Access10.1109/ACCESS.2024.345105412(121223-121241)Online publication date: 2024
    • (2024)A systematic literature review of recent advances on context-aware recommender systemsArtificial Intelligence Review10.1007/s10462-024-10939-458:1Online publication date: 16-Nov-2024
    • (2024)Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender SystemsAdvances in Information Retrieval10.1007/978-3-031-56027-9_9(140-156)Online publication date: 24-Mar-2024
    • (2023)Building a Multi-Objective Recommender System Using Machine Learning Based on Light Gradient-Boosting MachineProceedings of the 2023 6th International Conference on Big Data Technologies10.1145/3627377.3627387(60-65)Online publication date: 22-Sep-2023
    • (2023)Lightweight Boosting Models for User Response Prediction Using Adversarial ValidationProceedings of the Recommender Systems Challenge 202310.1145/3626221.3626225(9-13)Online publication date: 19-Sep-2023
    • (2022)A fuzzy content-based group recommender system with dynamic selection of the aggregation functionsInternational Journal of Approximate Reasoning10.1016/j.ijar.2022.08.015Online publication date: Sep-2022
    • (2022)Recommendations on Streaming Data: E-Tourism Event Stream Processing Recommender SystemIntelligent and Fuzzy Systems10.1007/978-3-031-09176-6_59(514-523)Online publication date: 2-Jul-2022
    • (2021)Booking.com Multi-Destination Trips DatasetProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463240(2457-2462)Online publication date: 11-Jul-2021
    • (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
    • (2020)GPU Accelerated Feature Engineering and Training for Recommender SystemsProceedings of the Recommender Systems Challenge 202010.1145/3415959.3415996(16-23)Online publication date: 26-Sep-2020
    • Show More Cited By

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