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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

Building an Agent for Texas Hold’em Poker Based on a Recommender System

January 23, 2011Amancio Bouza

Thomas Kaul just finished his bachelor’s thesis. The vision for his bachelor’s thesis was to create a computer program or rather an agent that plays Texas Hold’em to beat a specific human player in 1 vs 1. For instance, the… Continue Reading →

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Machine Learning, Recommender System, Research Gaming, Machine Learning, Recommender System, Thesis, User Profile

Establishing Healthy Two-Sided Online Markets with Ratings and Trust Inferencing

January 23, 2011Amancio Bouza

Matthias Z’brun just finished his master’s thesis. The goal of his master’s thesis was to investigate how, when, and when not ratings and trust inferencing in social networks force good behavior of suppliers and consumers in two-sided online markets. To… Continue Reading →

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Research Market, Platform, Research, Social Network Analysis, Thesis, Transaction, Trust, 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

Providing Movie Recommendations to Groups in Google Wave

January 14, 2010Amancio Bouza

Everybody is excited about Google Wave and curious about what new forms of interactions with other people become possible. One special thing about Google Wave are the Wavelets. Wavelets are a form of collaboration workspace where people chat and share… Continue Reading →

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App, Recommender System Application, Collaborative Filtering, Movie, Recommender System, Text Mining, User Profile, Web 2.0

SemTree: Ontology-Based Decision Tree Algorithm for Recommender Systems at the ISWC 2008

October 14, 2008Amancio Bouza

Our current work on a ontology-based decision tree algorithm to learn user preferences was accepted and published at 7th International Semantic Web Conference (ISWC) 2008 in Karlsruhe (Germany). The paper is available online at CEUR-WS.org as volume 401. It was… Continue Reading →

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Machine Learning, Recommender System, Research, Semantic Web Machine Learning, Ontology, Recommender System, Semantic Web, User Profile

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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