Paper-Conference

Defining and Measuring Fairness in Location Recommendations
Leonard Weydemann, Dimitris Sacharidis, Hannes Werthner
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Diversity and Novelty in Social-Based Collaborative Filtering
Dimitris Sacharidis
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Modeling Uncertainty in Group Recommendations
Dimitris Sacharidis
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The Impact of Social Connections in Personalization
Carine Pierrette Mukamakuza, Dimitris Sacharidis, Hannes Werthner
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Top-N group recommendations with fairness
When recommendations concern a group of people, rather than an invidivual, the system must also consider fairness within the group. That means each member of the group should receive roughly the same utility from the recommendations.
Dimitris Sacharidis
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Efficient progressive and diversified top-k best region search
Dimitrios Skoutas, Dimitris Sacharidis, Kostas Patroumpas
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Mining User Behavior in Social Recommender Systems
Carine Pierrette Mukamakuza, Dimitris Sacharidis, Hannes Werthner
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Selecting representative and diverse spatio-textual posts over sliding windows
Dimitris Sacharidis, Paras Mehta, Dimitrios Skoutas, Kostas Patroumpas, Agnès Voisard
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(μ)TOP: Spatio-Temporal Detection and Summarization of Locally Trending Topics in Microblog Posts
Paras Mehta, Manuel Kotlarski, Dimitrios Skoutas, Dimitris Sacharidis, Kostas Patroumpas, Agnès Voisard
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Continuous Summarization of Streaming Spatio-Textual Posts
Dimitris Sacharidis, Paras Mehta, Dimitrios Skoutas, Kostas Patroumpas, Agnès Voisard
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