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What makes a location really interesting and how to exploit it to improve location recommendations?

March 29, 2014Amancio Bouza

What makes a place or a location interesting for you? Well, your favorite bars, clubs or public places may cross your mind right now and you’ll think further to unique spots or sights in your local area or popular places… Continue Reading →

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App, Data Mining, Recommender System, Research, Visualization Application, Collaborative Filtering, Data Mining, iPhone, Location, Location Recommendation, Machine Learning, Mobile App, Recommender System, Research, User Profile, Visualization

Whose restaurant recommendation do you follow?

August 21, 2011Amancio Bouza

Michael Küchler just finished his master’s thesis. The goal of his master’s thesis was to investigate the benefit of following other people’s recommendations with similar or partially similar restaurant preferences. More specifically, do people follow the recommendations of other people who… Continue Reading →

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App, Data Mining, Recommender System Application, Collaborative Filtering, Data Mining, iPhone, Location, Location Recommendation, Machine Learning, Mobile App, Recommender System, Research, Thesis, User Profile

Environs: Visualization of Recommendation Clouds on the iPhone

September 5, 2010Amancio Bouza

Thomas Maurer just finished his master’s thesis. The goal of his master’s thesis was to use the location information about people with similar preferences rather than structured information about places to identify interesting locations. The underlying assumption is that locations… Continue Reading →

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App, Recommender System, Research, Visualization Application, Collaborative Filtering, Data Mining, iPhone, Location, Location Recommendation, Mobile App, Recommender System, Thesis, User Profile, Visualization

Recent Posts

  • Building the LEGO University of Zurich
  • What makes a location really interesting and how to exploit it to improve location recommendations?
  • Whose restaurant recommendation do you follow?
  • Applying Collaborative Filtering to Cross-Project Defect Prediction
  • Building an Agent for Texas Hold’em Poker Based on a Recommender System

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