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.

Personalized Recommendation of PoIs to People with Autism

Noemi Mauro;Liliana Ardissono;Federica Cena
2020

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.
28th ACM Conference on User Modeling, Adaptation and Personalization
Genova
12-18 July 2020
Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (UMAP '20)
ACM
163
172
978-1-4503-6861-2
https://dl.acm.org/doi/abs/10.1145/3340631.3394845
Recommender Systems, Autism Spectrum Disorder, accessibility
Noemi Mauro, Liliana Ardissono, Federica Cena
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1737470
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