Sharing geotagged pictures for an Emotion-based Recommender System. Hitz, A., Naas, S., & Sigg, S. In The 19th International Conference on Pervasive Computing and Communications (PerCom 2021), adjunct, 2021. abstract bibtex Recommender systems are prominently used for movie or app recommendation or in e-commerce by considering profiles, past preferences and increasingly also further personalized measures. We designed and implemented an emotion-based recommender system for city visitors that takes into account user emotion and user location for the recommendation process. We conducted a comparative study between the emotion-based recommender system and recommender systems based on traditional measures. Our evaluation study involved 28 participators and the experiments showed that the emotion-based recommender system increased the average rating of the recommendation by almost 19%. We conclude that the use of emotion can significantly improve the results and especially their level of personalization.
@inproceedings{hitz2020Emotion,
title={Sharing geotagged pictures for an Emotion-based Recommender System},
author={Andreas Hitz and Si-Ahmed Naas and Stephan Sigg},
booktitle={The 19th International Conference on Pervasive Computing and Communications (PerCom 2021), adjunct},
year={2021},
abstract={Recommender systems are prominently used for movie or app recommendation or in e-commerce by considering profiles, past preferences and increasingly also further personalized measures. We designed and implemented an emotion-based recommender system for city visitors that takes into account user emotion and user location for the recommendation process. We conducted a comparative study between the emotion-based recommender system and recommender systems based on traditional measures. Our evaluation study involved 28 participators and the experiments showed that the emotion-based recommender system increased the average rating of the recommendation by almost 19%. We conclude that the use of emotion can significantly improve the results and especially their level of personalization.
},
group = {ambience}
}
%%% 2020 %%%
Downloads: 0
{"_id":"pM7Yew8tseQSnGNf2","bibbaseid":"hitz-naas-sigg-sharinggeotaggedpicturesforanemotionbasedrecommendersystem-2021","author_short":["Hitz, A.","Naas, S.","Sigg, S."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Sharing geotagged pictures for an Emotion-based Recommender System","author":[{"firstnames":["Andreas"],"propositions":[],"lastnames":["Hitz"],"suffixes":[]},{"firstnames":["Si-Ahmed"],"propositions":[],"lastnames":["Naas"],"suffixes":[]},{"firstnames":["Stephan"],"propositions":[],"lastnames":["Sigg"],"suffixes":[]}],"booktitle":"The 19th International Conference on Pervasive Computing and Communications (PerCom 2021), adjunct","year":"2021","abstract":"Recommender systems are prominently used for movie or app recommendation or in e-commerce by considering profiles, past preferences and increasingly also further personalized measures. We designed and implemented an emotion-based recommender system for city visitors that takes into account user emotion and user location for the recommendation process. We conducted a comparative study between the emotion-based recommender system and recommender systems based on traditional measures. Our evaluation study involved 28 participators and the experiments showed that the emotion-based recommender system increased the average rating of the recommendation by almost 19%. We conclude that the use of emotion can significantly improve the results and especially their level of personalization. ","group":"ambience","bibtex":"@inproceedings{hitz2020Emotion,\n title={Sharing geotagged pictures for an Emotion-based Recommender System},\n author={Andreas Hitz and Si-Ahmed Naas and Stephan Sigg},\n booktitle={The 19th International Conference on Pervasive Computing and Communications (PerCom 2021), adjunct},\n year={2021},\n abstract={Recommender systems are prominently used for movie or app recommendation or in e-commerce by considering profiles, past preferences and increasingly also further personalized measures. We designed and implemented an emotion-based recommender system for city visitors that takes into account user emotion and user location for the recommendation process. We conducted a comparative study between the emotion-based recommender system and recommender systems based on traditional measures. Our evaluation study involved 28 participators and the experiments showed that the emotion-based recommender system increased the average rating of the recommendation by almost 19%. We conclude that the use of emotion can significantly improve the results and especially their level of personalization.\n},\n group = {ambience}\n}\n\n\n%%% 2020 %%%\n\n","author_short":["Hitz, A.","Naas, S.","Sigg, S."],"key":"hitz2020Emotion","id":"hitz2020Emotion","bibbaseid":"hitz-naas-sigg-sharinggeotaggedpicturesforanemotionbasedrecommendersystem-2021","role":"author","urls":{},"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"http://ambientintelligence.aalto.fi/bibtex/LiteraturAll","dataSources":["a6QYyvmdLfrsx7DiL","aPfcTvMp5jE2KuS7H"],"keywords":[],"search_terms":["sharing","geotagged","pictures","emotion","based","recommender","system","hitz","naas","sigg"],"title":"Sharing geotagged pictures for an Emotion-based Recommender System","year":2021}