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From More-Like-This to Better-Than-This: Hotel Recommendations from User Generated Reviews

Authors:

Ruihai Dong,

Barry SmythAuthors Info & Claims

UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

Pages 309 - 310

https://doi.org/10.1145/2930238.2930276

Published: 13 July 2016 Publication History

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Abstract

To help users discover relevant products and items recommender systems must learn about the likes and dislikes of users and the pros and cons of items. In this paper, we present a novel approach to building rich feature-based user profiles and item descriptions by mining user-generated reviews. We show how this information can be integrated into recommender systems to deliver better recommendations and an improved user experience.

References

[1]

Chen, L., Chen, G., and Wang, F. Recommender systems based on user reviews: the state of the art. User Modeling and User-Adapted Interaction 25, 2 (2015), 99--154.

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[2]

Dong, R., O'Mahony, M. P., Schaal, M., McCarthy, K., and Smyth, B. Combining similarity and sentiment in opinion mining for product recommendation. Journal of Intelligent Information Systems (2015), 1--28.

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Google Scholar

[3]

Dong, R., O'Mahony, M. P., and Smyth, B. Further Experiments in Opinionated Product Recommendation. In Proceedings of The 22nd International Conference on Case-Based Reasoning (Cork, Ireland, Sept. 2014), 110--124.

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Musat, C.-C., Liang, Y., and Faltings, B. Recommendation using textual opinions. In IJCAI International Joint Conference on Artificial Intelligence, IJCAI '13, AAAI Press (2013), 2684--2690.

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Cited By

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Subramanian RPoojith Reddy MKousik KRupesh SRohith SSai Kumar B(2022)ClassHotel: Application of Data Analytic Techniques for Online Hotel Recommendation2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)10.1109/ICACRS55517.2022.10029329(1021-1026)Online publication date: 13-Dec-2022
https://doi.org/10.1109/ICACRS55517.2022.10029329
Dong XLi TSong RDing Z(2020)Profiling users via their reviews: an extended systematic mapping studySoftware and Systems Modeling10.1007/s10270-020-00790-wOnline publication date: 19-Mar-2020
https://doi.org/10.1007/s10270-020-00790-w
Papilaris MChalkiadakis G(2019)Markov Chain Monte Carlo for Effective Personalized RecommendationsMulti-Agent Systems10.1007/978-3-030-14174-5_13(188-204)Online publication date: 15-Feb-2019
https://doi.org/10.1007/978-3-030-14174-5_13
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Index Terms

From More-Like-This to Better-Than-This: Hotel Recommendations from User Generated Reviews
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
      2. Sentiment analysis
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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Information & Contributors

Information

Published In

UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

July 2016

366 pages

ISBN:9781450343688

DOI:10.1145/2930238

General Chairs:
Julita Vassileva
University of Saskatchewan, Canada
,
James Blustein
Dalhousie University, Canada
,
Program Chairs:
Lora Aroyo
VU Amsterdam, the Netherlands
,
Sidney D'Mello
University of Notre Dame, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2016

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Poster

Funding Sources

Science Foundation Ireland

Conference

UMAP '16

Sponsor:

UMAP '16: User Modeling, Adaptation and Personalization Conference

July 13 - 17, 2016

Nova Scotia, Halifax, Canada

Acceptance Rates

UMAP '16 Paper Acceptance Rate 21 of 123 submissions, 17%;

Overall Acceptance Rate 162 of 633 submissions, 26%

Upcoming Conference

UMAP '25

Sponsor:
sigchi
sigchi

33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

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Cited By

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Subramanian RPoojith Reddy MKousik KRupesh SRohith SSai Kumar B(2022)ClassHotel: Application of Data Analytic Techniques for Online Hotel Recommendation2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)10.1109/ICACRS55517.2022.10029329(1021-1026)Online publication date: 13-Dec-2022
https://doi.org/10.1109/ICACRS55517.2022.10029329
Dong XLi TSong RDing Z(2020)Profiling users via their reviews: an extended systematic mapping studySoftware and Systems Modeling10.1007/s10270-020-00790-wOnline publication date: 19-Mar-2020
https://doi.org/10.1007/s10270-020-00790-w
Papilaris MChalkiadakis G(2019)Markov Chain Monte Carlo for Effective Personalized RecommendationsMulti-Agent Systems10.1007/978-3-030-14174-5_13(188-204)Online publication date: 15-Feb-2019
https://doi.org/10.1007/978-3-030-14174-5_13
Wang JZhang XZhang H(2018)Hotel recommendation approach based on the online consumer reviews using interval neutrosophic linguistic numbersJournal of Intelligent & Fuzzy Systems10.3233/JIFS-17142134:1(381-394)Online publication date: 12-Jan-2018
https://doi.org/10.3233/JIFS-171421
Lee JAwayan PMendoza E(2017)A Comparative StudyProceedings of the 2017 International Conference on Software and e-Business10.1145/3178212.3178235(28-32)Online publication date: 28-Dec-2017
https://dl.acm.org/doi/10.1145/3178212.3178235
Takuma KYamamoto JKamei SFujita S(2016)A Hotel Recommendation System Based on Reviews: What Do You Attach Importance To?2016 Fourth International Symposium on Computing and Networking (CANDAR)10.1109/CANDAR.2016.0129(710-712)Online publication date: Nov-2016
https://doi.org/10.1109/CANDAR.2016.0129

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Recommendations

Who predicts better?: results from an online study comparing humans and an online recommender system

Flatter Is Better: Percentile Transformations for Recommender Systems

Utilizing Favorites Lists for Better Recommendations