@inproceedings{Mauro:21c,
  author = {Noemi Mauro and Liliana Ardissono and Federica Cena and Stefano Cocomazzi},
  title = {Information Extraction for Inclusive Recommender Systems},
  year = {2021},
  booktitle = {Joint Proceedings of the ACM IUI 2021 Workshops - Workshop on Social and Cultural Interaction with Personalized Interfaces (SOCIALIZE)},
  address = {Online},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2903},
  publisher = {CEUR-WS.org},
  url_Link       = {http://ceur-ws.org/Vol-2903/IUI21WS-SOCIALIZE-7.pdf},
  url_Paper = {2021_SOCIALIZE_Autism},
  doi       = {http://ceur-ws.org/Vol-2903/IUI21WS-SOCIALIZE-7.pdf},
  keywords = {Recommender Systems, Geographic Information Systems, People with Autism},
  abstract = {Inclusive recommender systems should take both user preferences and the compatibility of items with the user into account in order to generate suggestions that can be appreciated and smoothly experienced at the same time. For instance, considering people in the Autism Spectrum Disorder, the sensory features of a place that is potentially interesting to the user are important to predict whether it might make her/him uncomfortable when visiting it. However, information about users’ experience with items can
hardly be found in the metadata provided by online geographic sources.
In order to address this issue, we suggest to retrieve it from the consumer feedback collected by location-based services that publish item reviews. This type of feedback represents a sustainable information source because it is supported by people through a continuous reviewing activity. Thus, it deserves special attention as a potential data source. In this paper, we outline how this type of information can be retrieved and we discuss its benefits to Top-N recommendation of Points of Interest.
}
}

{"_id":"6bAsfPfsNfnPrA4Fx","bibbaseid":"mauro-ardissono-cena-cocomazzi-informationextractionforinclusiverecommendersystems-2021","author_short":["Mauro, N.","Ardissono, L.","Cena, F.","Cocomazzi, S."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Noemi"],"propositions":[],"lastnames":["Mauro"],"suffixes":[]},{"firstnames":["Liliana"],"propositions":[],"lastnames":["Ardissono"],"suffixes":[]},{"firstnames":["Federica"],"propositions":[],"lastnames":["Cena"],"suffixes":[]},{"firstnames":["Stefano"],"propositions":[],"lastnames":["Cocomazzi"],"suffixes":[]}],"title":"Information Extraction for Inclusive Recommender Systems","year":"2021","booktitle":"Joint Proceedings of the ACM IUI 2021 Workshops - Workshop on Social and Cultural Interaction with Personalized Interfaces (SOCIALIZE)","address":"Online","series":"CEUR Workshop Proceedings","volume":"2903","publisher":"CEUR-WS.org","url_link":"http://ceur-ws.org/Vol-2903/IUI21WS-SOCIALIZE-7.pdf","url_paper":"2021_SOCIALIZE_Autism","doi":"http://ceur-ws.org/Vol-2903/IUI21WS-SOCIALIZE-7.pdf","keywords":"Recommender Systems, Geographic Information Systems, People with Autism","abstract":"Inclusive recommender systems should take both user preferences and the compatibility of items with the user into account in order to generate suggestions that can be appreciated and smoothly experienced at the same time. For instance, considering people in the Autism Spectrum Disorder, the sensory features of a place that is potentially interesting to the user are important to predict whether it might make her/him uncomfortable when visiting it. However, information about users’ experience with items can hardly be found in the metadata provided by online geographic sources. In order to address this issue, we suggest to retrieve it from the consumer feedback collected by location-based services that publish item reviews. This type of feedback represents a sustainable information source because it is supported by people through a continuous reviewing activity. Thus, it deserves special attention as a potential data source. In this paper, we outline how this type of information can be retrieved and we discuss its benefits to Top-N recommendation of Points of Interest. ","bibtex":"@inproceedings{Mauro:21c,\r\n author = {Noemi Mauro and Liliana Ardissono and Federica Cena and Stefano Cocomazzi},\r\n title = {Information Extraction for Inclusive Recommender Systems},\r\n year = {2021},\r\n booktitle = {Joint Proceedings of the ACM IUI 2021 Workshops - Workshop on Social and Cultural Interaction with Personalized Interfaces (SOCIALIZE)},\r\n address = {Online},\r\n series = {{CEUR} Workshop Proceedings},\r\n volume = {2903},\r\n publisher = {CEUR-WS.org},\r\n url_Link = {http://ceur-ws.org/Vol-2903/IUI21WS-SOCIALIZE-7.pdf},\r\n url_Paper = {2021_SOCIALIZE_Autism},\r\n doi = {http://ceur-ws.org/Vol-2903/IUI21WS-SOCIALIZE-7.pdf},\r\n keywords = {Recommender Systems, Geographic Information Systems, People with Autism},\r\n abstract = {Inclusive recommender systems should take both user preferences and the compatibility of items with the user into account in order to generate suggestions that can be appreciated and smoothly experienced at the same time. For instance, considering people in the Autism Spectrum Disorder, the sensory features of a place that is potentially interesting to the user are important to predict whether it might make her/him uncomfortable when visiting it. However, information about users’ experience with items can\r\nhardly be found in the metadata provided by online geographic sources.\r\nIn order to address this issue, we suggest to retrieve it from the consumer feedback collected by location-based services that publish item reviews. This type of feedback represents a sustainable information source because it is supported by people through a continuous reviewing activity. Thus, it deserves special attention as a potential data source. In this paper, we outline how this type of information can be retrieved and we discuss its benefits to Top-N recommendation of Points of Interest.\r\n}\r\n}\r\n\r\n\r\n","author_short":["Mauro, N.","Ardissono, L.","Cena, F.","Cocomazzi, S."],"key":"Mauro:21c","id":"Mauro:21c","bibbaseid":"mauro-ardissono-cena-cocomazzi-informationextractionforinclusiverecommendersystems-2021","role":"author","urls":{" link":"http://ceur-ws.org/Vol-2903/IUI21WS-SOCIALIZE-7.pdf"," paper":"http://www.di.unito.it/~mauro/publications/2021_SOCIALIZE_Autism"},"keyword":["Recommender Systems","Geographic Information Systems","People with Autism"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"inproceedings","biburl":"http://www.di.unito.it/~mauro/publications/publications.bib","dataSources":["oAX9E8fudN9Wi4Fok"],"keywords":["recommender systems","geographic information systems","people with autism"],"search_terms":["information","extraction","inclusive","recommender","systems","mauro","ardissono","cena","cocomazzi"],"title":"Information Extraction for Inclusive Recommender Systems","year":2021}