Article

Applicability of Demographic Recommender System to Tourist Attractions: A Case Study on Trip Advisor

Authors:

Stephen Chi-Fai Chan,

Grace NgaiAuthors Info & Claims

WI-IAT '12: Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03

Pages 97 - 101

https://doi.org/10.1109/WI-IAT.2012.133

Published: 04 December 2012 Publication History

Abstract

Most of the existing recommender systems for tourism apply knowledge-based and content-based approaches, which need sufficient historical rating information or extra knowledge and suffer from the cold start problem. In this paper, a demographic recommender system is utilized for the recommendation of attractions. This system categorizes the tourists using their demographic information and then makes recommendations based on demographic classes. Its advantage is that the history of ratings and extra knowledge are not needed, so a new tourist can obtain recommendation. Focusing on the attractions on Trip Advisor, we use different machine learning methods to produce prediction of ratings, so as to determine whether these approaches and demographic information of tourists are suitable for providing recommendations. Our preliminary results show that the methods and demographic information can be used to predict tourists' ratings on attractions. But using demographic information alone can only achieve limited accuracy. More information such as textual reviews is required to improve the accuracy of the recommendation.

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Cited By

Kulvinder Singh Dhawan SBali N(2024)An Ensemble Learning Hybrid Recommendation System Using Content-Based, Collaborative Filtering, Supervised Learning and Boosting AlgorithmsAutomatic Control and Computer Sciences10.3103/S014641162470061558:5(491-505)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.3103/S0146411624700615
van Capelleveen GAmrit CYazan DZijm H(2022)The recommender canvasExpert Systems with Applications: An International Journal10.1016/j.eswa.2019.04.001129:C(97-117)Online publication date: 20-Apr-2022
https://dl.acm.org/doi/10.1016/j.eswa.2019.04.001
Anik ABunt AKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote TransparencyProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445736(1-13)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445736
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Index Terms

Applicability of Demographic Recommender System to Tourist Attractions: A Case Study on Trip Advisor
1. Applied computing
  1. Operations research
    1. Decision analysis
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Expert systems

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cover image ACM Conferences

WI-IAT '12: Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03

December 2012

417 pages

ISBN:9780769548807

Sponsors

SIGAI: ACM Special Interest Group on Artificial Intelligence

Publisher

IEEE Computer Society

United States

Publication History

Published: 04 December 2012

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Cited By

Kulvinder Singh Dhawan SBali N(2024)An Ensemble Learning Hybrid Recommendation System Using Content-Based, Collaborative Filtering, Supervised Learning and Boosting AlgorithmsAutomatic Control and Computer Sciences10.3103/S014641162470061558:5(491-505)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.3103/S0146411624700615
van Capelleveen GAmrit CYazan DZijm H(2022)The recommender canvasExpert Systems with Applications: An International Journal10.1016/j.eswa.2019.04.001129:C(97-117)Online publication date: 20-Apr-2022
https://dl.acm.org/doi/10.1016/j.eswa.2019.04.001
Anik ABunt AKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote TransparencyProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445736(1-13)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445736
Kamel MGil-Solla ARamos-Carber M(2020)Tasks Recommendation in Crowdsourcing based on Workers' Implicit Profiles and Performance HistoryProceedings of the 9th International Conference on Software and Information Engineering10.1145/3436829.3436834(51-55)Online publication date: 11-Nov-2020
https://dl.acm.org/doi/10.1145/3436829.3436834
She KHaw SChew L(2020)Tourism Recommender System Utilising Property Graph Ontology as Knowledge BaseProceedings of the 12th International Conference on Computer Modeling and Simulation10.1145/3408066.3408102(14-18)Online publication date: 22-Jun-2020
https://dl.acm.org/doi/10.1145/3408066.3408102
Tao LCao JLiu F(2018)Dynamic feature weighting based on user preference sensitivity for recommender systemsKnowledge-Based Systems10.1016/j.knosys.2018.02.019149:C(61-75)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1016/j.knosys.2018.02.019
Fan XChen ZZhu LLiao ZFu B(2017)A Novel Hybrid Similarity Calculation ModelScientific Programming10.1155/2017/43791412017Online publication date: 4-Dec-2017
https://dl.acm.org/doi/10.1155/2017/4379141
García LGonzález-Calleros J(2017)A user modeling approach to personalized sightseeing toursProceedings of the XVIII International Conference on Human Computer Interaction10.1145/3123818.3123875(1-8)Online publication date: 25-Sep-2017
https://dl.acm.org/doi/10.1145/3123818.3123875
Smirnov AShilov NKashevnik APonomarev A(2016)Cyber-physical infomobility for tourism applicationInternational Journal of Information Technology and Management10.1504/IJITM.2017.08094916:1(31-52)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1504/IJITM.2017.080949
Elahi MRicci FRubens N(2016)A survey of active learning in collaborative filtering recommender systemsComputer Science Review10.1016/j.cosrev.2016.05.00220:C(29-50)Online publication date: 1-May-2016
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