Archive for the Recommendations category

Senjuti Basu Roy
Senjuti Basu Roy

Returning Top-K : Preference Aggregation or Sortition, or is there a Better Middle Ground?

Recommendations

Given a large number of users’ preferences (numerical or ordinal scores, ranked order) over a large number of objects,  returning top-k results entails selecting a small list/set containing exactly k objects that are most “appropriate “. In this article,  I will investigate two alternatives for selecting a top-k list/set that consumes such preference based inputs. […]

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Mohamed Mokbel
Mohamed Mokbel

Thinking Spatial

Databases, Recommendations, Spatial, Systems

Self-driving cars, ride-sharing service (e.g., Uber and Lyft), and Pokemon Go are just three examples of recent disruptive applications that gained huge market share and publicity. It is expected that each self-driving car will generate 2 PB of data per year, with 10 Million of such cars by 2020. Uber has 2+ Billion rides so […]

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Kostas Stefanidis, Eirini Ntoutsi, Haridimos Kondylakis
Kostas Stefanidis, Eirini Ntoutsi, Haridimos Kondylakis

Information Hunting: The Many Faces of Recommendations for Data Exploration

Recommendations

With the growing complexity of the Web, users often find themselves overwhelmed by the mass of choices available. For example, shopping for DVDs or clothes online becomes more and more difficult, as the variety of offers increases rapidly and gets unmanageable. To facilitate users in their selection process, recommender systems provide suggestions of potential interest […]

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