Matchmaking is an age-old concept that has been revolutionalized with the
advent of Internet. Suddenly the pool of potential partners that one can
plausibly consider has exploded. Thanks to the move of courtship online we have
collected unprecedented amounts of data on romantic interactions.
If you are looking for love you may want to take advatage of this accumulated
knowledge to give yourself a leg up. However making causal inferences aka
“Dating advice” can be problematic due to various sample biases. I will instead
show how this data can be leveraged to build a matchmaking system that reduces
data overload and improves your chances of a happy marriage.
In this presentation I will focus specifically at solving three problems with data:
I will show how hadoop, vowpal wabbit, gbms, and graph optimization can be used
together to solve the matchmaking problem as well as related problems in
advertising and constrained recommendation. The presentation will aslo
highlight the architecture of eHarmony’s matchmaking system that every night
needs to choose the best set of introductions from about 2^10^12 possibilities.
Vaclav Petricek is a Principal Data Scientist at Santa Monica based eHarmony where he is responsible for optimization, and machine learning applications for eHarmony core matchmaking algorithms. He also runs a series of invited ML talks at eHarmony, part of the Los Angeles Machine Learning Meetup. Prior to eHarmony, Vaclav was visiting Researcher at University College London where his research spanned recommender systems, social networks, web structure and online auctions. Prior to that he has worked at several Czech startups as a developer and sysadmin. He earned his PhD in Computer Science from Charles University in Prague as well as his Masters in Distributed Systems.
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