ColFi - Recommender System for a Dating Service

Mgr. Lukáš Brožovský, Charles University in Prague, Faculty of Mathematics and Physics, Department of Software Engineering
Supervised by RNDr. Václav Petříček

Abstract: The aim of the thesis is to research the utility of collaborative filtering based recommender systems in the area of dating services. The practical part of the thesis describes the actual implementation of several standard collaborative filtering algorithms and a system, which recommends potential personal matches to users based on their preferences (e.g. ratings of other user profiles). The collaborative filtering is built upon the assumption, that users with similar rating patterns will also rate alike in the future.
  Second part of the work focuses on several benchmarks of the implemented system's accuracy and performance on publicly available data sets (MovieLens and Jester) and also on data sets originating from real online dating services (ChceteMě and LíbímSeTi). All benchmark results proved that collaborative filtering technique could be successfully used in the area of online dating services.

Keywords: recommender system, collaborative filtering, matchmaking, online dating service, k-nearest neighbours algorithm, Pearson's correlation coefficient, Java


Download the thesis  (PDF, english, 1.5 MB)

Download the paper presented at ZNALOSTI 2007 conference  (PDF, english, 257 KB)

Download the thesis presentation  (PDF, czech, 152 KB)

Download the image of ColFi binary distribution  (7zipped ISO, english, 63 MB)

Visit the download page for complete LíbímSeTi data set  (CSV)

Visit the download page for ColFi source code at  (Java)


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