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 SourceForge.net (Java)