Titlebook: Bias and Social Aspects in Search and Recommendation; First International Ludovico Boratto,Stefano Faralli,Giovanni Stilo Conference proce

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Venue Suggestion Using Social-Centric Scores,rom venues that they have previously visited. In particular, we focus on scores extracted from social information available on location-based social networks. Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, show that social scores are more effective than scores based venues’ content.

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,The Impact of Foursquare Checkins on Users’ Emotions on Twitter,nues on Foursquare can impact users’ online emotion expression as depicted in their tweets. We show that users’ offline activity can impact users’ online emotions; however, the type of activity determines the extent to which a user’s emotions will be impacted.

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Ming Yang,Zhizong Wu,Zhiguo Yanrelevance, diversity, and related concepts. Then, it focuses on explaining the emerging concept of fairness in various recommendation settings. In doing so, this paper presents comparisons and highlights contracts among various measures, and gaps in our conceptual and evaluative frameworks.

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https://doi.org/10.1007/978-981-19-8136-4nking the recommendations under preferential fairness constraints. Our experimental results demonstrate that the state of fairness can be reached with minimal accuracy compromises for both binary and non-binary attributes.