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.
{"title":"Personalized Recommendation of PoIs to People with Autism","authors":"Noemi Mauro, L. Ardissono, F. Cena","doi":"10.1145/3340631.3394845","DOIUrl":"https://doi.org/10.1145/3340631.3394845","url":null,"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.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123262848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the uptake of algorithmic personalization in the news domain, news organizations increasingly trust automated systems with previously considered editorial responsibilities, e.g., prioritizing news to readers. In this paper we study an automated news recommender system in the context of a news organization's editorial values. We conduct and present two online studies with a news recommender system, which span one and a half months and involve over 1,200 users. In our first study we explore how our news recommender steers reading behavior in the context of editorial values such as serendipity, dynamism, diversity, and coverage. Next, we present an intervention study where we extend our news recommender to steer our readers to more dynamic reading behavior. We find that (i) our recommender system yields more diverse reading behavior and yields a higher coverage of articles compared to non-personalized editorial rankings, and (ii) we can successfully incorporate dynamism in our recommender system as a re-ranking method, effectively steering our readers to more dynamic articles without hurting our recommender system's accuracy.
{"title":"Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation","authors":"Feng Lu, Anca Dumitrache, David Graus","doi":"10.1145/3340631.3394864","DOIUrl":"https://doi.org/10.1145/3340631.3394864","url":null,"abstract":"With the uptake of algorithmic personalization in the news domain, news organizations increasingly trust automated systems with previously considered editorial responsibilities, e.g., prioritizing news to readers. In this paper we study an automated news recommender system in the context of a news organization's editorial values. We conduct and present two online studies with a news recommender system, which span one and a half months and involve over 1,200 users. In our first study we explore how our news recommender steers reading behavior in the context of editorial values such as serendipity, dynamism, diversity, and coverage. Next, we present an intervention study where we extend our news recommender to steer our readers to more dynamic reading behavior. We find that (i) our recommender system yields more diverse reading behavior and yields a higher coverage of articles compared to non-personalized editorial rankings, and (ii) we can successfully incorporate dynamism in our recommender system as a re-ranking method, effectively steering our readers to more dynamic articles without hurting our recommender system's accuracy.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123415464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adaptive systems are usually interactive systems. As such they stand to benefit considerably from a development lifecycle that ensures user involvement from the early design stages, and embraces evaluation, in both formative and summative forms. Evaluating an adaptive system involves a number of specific problems and pitfalls that need to be addressed by the selection of specific methods, techniques and criteria. This tutorial aims to introduce participants to the peculiarities that arise when evaluating adaptive interactive systems. A layered evaluation framework is used to separate the evaluation process into a number of different aspects which can be applied at different stages throughout the development life-cycle.
{"title":"Evaluation of Adaptive Systems","authors":"Stephan Weibelzahl","doi":"10.1145/3340631.3398668","DOIUrl":"https://doi.org/10.1145/3340631.3398668","url":null,"abstract":"Adaptive systems are usually interactive systems. As such they stand to benefit considerably from a development lifecycle that ensures user involvement from the early design stages, and embraces evaluation, in both formative and summative forms. Evaluating an adaptive system involves a number of specific problems and pitfalls that need to be addressed by the selection of specific methods, techniques and criteria. This tutorial aims to introduce participants to the peculiarities that arise when evaluating adaptive interactive systems. A layered evaluation framework is used to separate the evaluation process into a number of different aspects which can be applied at different stages throughout the development life-cycle.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131304117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","authors":"","doi":"10.1145/3340631","DOIUrl":"https://doi.org/10.1145/3340631","url":null,"abstract":"","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122528777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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