research-article

Personalized Recommendation of PoIs to People with Autism

Authors:

Liliana Ardissono,

Federica CenaAuthors Info & Claims

UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

Pages 163 - 172

https://doi.org/10.1145/3340631.3394845

Published: 13 July 2020 Publication History

Abstract

The suggestion of Points of Interest to people with Autism Spectrum Disorder (ASD) challenges recommender systems research because these users' perception of places is influenced by idiosyncratic sensory aversions which can mine their experience by causing stress and anxiety. Therefore, managing individual preferences is not enough to provide these people with suitable recommendations. In order to address this issue, we propose a Top-N recommendation model that combines the user's idiosyncratic aversions with her/his preferences in a personalized way to suggest the most compatible and likable Points of Interest for her/him. We are interested in finding a user-specific balance of compatibility and interest within a recommendation model that integrates heterogeneous evaluation criteria to appropriately take these aspects into account. We tested our model on both ASD and "neurotypical" people. The evaluation results show that, on both groups, our model outperforms in accuracy and ranking capability the recommender systems based on item compatibility, on user preferences, or which integrate these two aspects by means of a uniform evaluation model.

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Cited By

Pera MCena FHuibers TLandoni MMauro NMurgia E(2024)1$$^{st}$$ Workshop on Information Retrieval for Understudied Users (IR4U2)Advances in Information Retrieval10.1007/978-3-031-56069-9_55(409-414)Online publication date: 23-Mar-2024
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Index Terms

Personalized Recommendation of PoIs to People with Autism

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cover image ACM Conferences

UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

July 2020

426 pages

ISBN:9781450368612

DOI:10.1145/3340631

Editors:
Tsvi Kuflik
University of Haifa, Israel
,
Ilaria Torre
University of Genoa, Italy
,
Robin Burke
University of Colorado, Boulder, USA
,
Cristina Gena
University of Turin, Italy

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 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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Published: 13 July 2020

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UMAP '20: 28th ACM Conference on User Modeling, Adaptation and Personalization

July 14 - 17, 2020

Genoa, Italy

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Cited By

Pera MCena FHuibers TLandoni MMauro NMurgia E(2024)1$$^{st}$$ Workshop on Information Retrieval for Understudied Users (IR4U2)Advances in Information Retrieval10.1007/978-3-031-56069-9_55(409-414)Online publication date: 23-Mar-2024
https://doi.org/10.1007/978-3-031-56069-9_55
Ekstrand MBeattie LPera MCramer H(2024)Not Just Algorithms: Strategically Addressing Consumer Impacts in Information RetrievalAdvances in Information Retrieval10.1007/978-3-031-56066-8_25(314-335)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56066-8_25
Deng LRattadilok P(2022)A Sensor and Machine Learning-Based Sensory Management Recommendation System for Children with Autism Spectrum DisordersSensors10.3390/s2215580322:15(5803)Online publication date: 3-Aug-2022
https://doi.org/10.3390/s22155803
Ardissono LTorta GBarone PSegnan MMattutino CBellogin ABoratto LSantos OArdissono LKnijnenburg B(2022)Integration of Cultural and Natural Heritage Information in Future Mobile GuidesAdjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3511047.3537648(402-405)Online publication date: 4-Jul-2022
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Khan NMa ZUllah APolat K(2022)Categorization of knowledge graph based recommendation methods and benchmark datasets from the perspectives of application scenarios: A comprehensive surveyExpert Systems with Applications10.1016/j.eswa.2022.117737206(117737)Online publication date: Nov-2022
https://doi.org/10.1016/j.eswa.2022.117737
Mauro NArdissono LCocomazzi SCena F(2022)Using consumer feedback from location-based services in PoI recommender systems for people with autismExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116972199:COnline publication date: 23-May-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116972
Mcheick HEzzeddine FLakkis FMsheik BEzzeddine M(2022)Tantrum-Track: Context and Ontological Representation Model for Recommendation and Tracking Services for People with AutismIntelligent Systems and Applications10.1007/978-3-031-16075-2_46(620-635)Online publication date: 1-Sep-2022
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Deng LRattadilok PXiong R(2021)A Machine Learning-Based Monitoring System for Attention and Stress Detection for Children with Autism Spectrum DisordersProceedings of the 2021 International Conference on Intelligent Medicine and Health10.1145/3484377.3484381(23-29)Online publication date: 13-Aug-2021
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Mauro NArdissono LPetrone GGeninatti Cossatin AMattutino C(2021)Beyond Traditional Cultural Heritage Recommender Systems: Suggesting Airbnb Experiences to UsersAdjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450614.3463385(203-207)Online publication date: 21-Jun-2021
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Schelenz L(2021)Diversity-aware Recommendations for Social Justice? Exploring User Diversity and Fairness in Recommender SystemsAdjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450614.3463293(404-410)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450614.3463293
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