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This article presents an affective based sensemaking system for grouping and suggesting stories created by the users about the items of a museum. By relying on the TCL commonsense reasoning framework1, the system exploits the spatial structure of the Plutchik's wheel of emotions to organize the stories according to their extracted emotions. The process of emotion extraction, reasoning and suggestion is triggered by an app, called GAMGame, and integrated with the sensemaking engine. Following the framework of Citizen Curation, the system allows classifying and suggesting stories encompassing cultural items able to evoke not only the very same emotions of already experienced or preferred museum objects, but also novel items sharing different emotional stances and, therefore, able to break the filter bubble effect and open the users' view towards more inclusive and empathy-based interpretations of cultural content. The system has been designed tested, in the context of the H2020EU SPICE project (Social cohesion, Participation, and Inclusion through Cultural Engagement), in cooperation the community of the d/Deaf and on the collection of the Gallery of Modern Art (GAM) in Turin. We describe the user centered design process of the web app and of its components and we report the results concerning the effectiveness of the of the diversity seeking, affective driven, recommendations of stories.
49 pages
FOS: Computer and information sciences, Computer Science - Human-Computer Interaction, story based recommendations, recommender systems, affective computing, 650, diversity-seeking emotional recommendation, human computer interaction, recommender systems, affective computing, 004, Human-Computer Interaction (cs.HC), Story-based recommendations; diversity-seeking emotional recommendations; commonsense reasoning; affective computing; recommender systems, Story-based recommendation, commonsense reasoning, recommender systems, affective computing, https://dl.acm.org/ccs
FOS: Computer and information sciences, Computer Science - Human-Computer Interaction, story based recommendations, recommender systems, affective computing, 650, diversity-seeking emotional recommendation, human computer interaction, recommender systems, affective computing, 004, Human-Computer Interaction (cs.HC), Story-based recommendations; diversity-seeking emotional recommendations; commonsense reasoning; affective computing; recommender systems, Story-based recommendation, commonsense reasoning, recommender systems, affective computing, https://dl.acm.org/ccs
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 10 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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