Archive for the Recommendations category

Mohamed Sharaf

Utilizing the Synergy between Query Refinement and View Recommendation for Visual Data Exploration

data exploration, Databases, Recommendations, Visualization

In this blog, we discuss the potential benefits of augmenting automated view recommendation solutions with query refinement techniques towards achieving insightful data exploration. Particularly, effective data exploration has been fueled by many approaches that rely on either view recommendation or query refinement, as two separate and independent techniques for gaining valuable insights from data. In […]

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Senjuti Basu Roy

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


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

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

Information Hunting: The Many Faces of Recommendations for Data Exploration


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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