research-article

RecSeats: A Hybrid Convolutional Neural Network Choice Model for Seat Recommendations at Reserved Seating Venues

Authors:

Simon J. BlanchardAuthors Info & Claims

RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems

Pages 309 - 317

https://doi.org/10.1145/3383313.3412263

Published: 22 September 2020 Publication History

Abstract

Predicting locational choices (i.e., where one chooses to sit) is a challenging task because preferences are highly heterogeneous and depend not only on the location of the seats in the environment but also on the location of others. In the present research, we propose RecSeats - a framework to predict locational choices. The framework augments individual-level discrete choice models with a convolutional neural network (CNN) which can capture higher order interactions between features of available seats. The framework is flexible and can accommodate complexity in real-world locational choice data such as variability in the number of tickets purchased and the number and locations from past purchases. Applied to both locational choice experiment data and to ticketing data from a large North-American concert hall, we show that augmenting individual-level discrete choice models with a CNN consistently provides strong predictive accuracy.

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Cited By

Lin CChen SZeng MZhang SGao MLi H(2024)Shilling Black-Box Recommender Systems by Learning to Generate Fake User ProfilesIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318321035:1(1305-1319)Online publication date: Jan-2024
https://doi.org/10.1109/TNNLS.2022.3183210
Wang SBian HLi S(2024)Hybrid Travel Recommendation Algorithm based on Center Aggregation Parameters2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)10.1109/ICICACS60521.2024.10498244(1-7)Online publication date: 23-Feb-2024
https://doi.org/10.1109/ICICACS60521.2024.10498244
Nanty SFiig TZannier LDefoin-Platel M(2024)Enhanced demand forecasting by combining analytical models and machine learning modelsJournal of Revenue and Pricing Management10.1057/s41272-024-00490-wOnline publication date: 30-Aug-2024
https://doi.org/10.1057/s41272-024-00490-w
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cover image ACM Conferences

RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems

September 2020

796 pages

ISBN:9781450375832

DOI:10.1145/3383313

Copyright © 2020 ACM.

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 ACM 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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Publication History

Published: 22 September 2020

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Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada

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RecSys '20

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RecSys '20: Fourteenth ACM Conference on Recommender Systems

September 22 - 26, 2020

Virtual Event, Brazil

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Lin CChen SZeng MZhang SGao MLi H(2024)Shilling Black-Box Recommender Systems by Learning to Generate Fake User ProfilesIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318321035:1(1305-1319)Online publication date: Jan-2024
https://doi.org/10.1109/TNNLS.2022.3183210
Wang SBian HLi S(2024)Hybrid Travel Recommendation Algorithm based on Center Aggregation Parameters2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)10.1109/ICICACS60521.2024.10498244(1-7)Online publication date: 23-Feb-2024
https://doi.org/10.1109/ICICACS60521.2024.10498244
Nanty SFiig TZannier LDefoin-Platel M(2024)Enhanced demand forecasting by combining analytical models and machine learning modelsJournal of Revenue and Pricing Management10.1057/s41272-024-00490-wOnline publication date: 30-Aug-2024
https://doi.org/10.1057/s41272-024-00490-w
Hazrati NRicci F(2023)Choice models and recommender systems effects on users’ choicesUser Modeling and User-Adapted Interaction10.1007/s11257-023-09366-x34:1(109-145)Online publication date: 18-May-2023
https://doi.org/10.1007/s11257-023-09366-x
Chia PTagliabue JBianchi FHe CKo B(2022)Beyond NDCG: Behavioral Testing of Recommender Systems with RecListCompanion Proceedings of the Web Conference 202210.1145/3487553.3524215(99-104)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3487553.3524215
Bernardis CDacrema MMaurera FQuadrana MScriminaci MCremonesi P(2022)From Data Analysis to Intent-Based Recommendation: An Industrial Case Study in the Video DomainIEEE Access10.1109/ACCESS.2022.314843410(14779-14796)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3148434
Nielsen DYang NDube LKnäuper BLing YNie J(undefined)Consumption Variety in Food RecommendationSSRN Electronic Journal10.2139/ssrn.3707438
https://doi.org/10.2139/ssrn.3707438

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