#6: recsys with matrix factorization and information extraction from documents


Details
Our schedule for this time:
- Recommender systems with matrix factorization (Abhishek Thakur (https://twitter.com/abhi1thakur))
In this talk, we will discuss the basics of recommendation systems and how a simple recommendation system can be constructed using a nearest neighbor method. This will be extended to recommendation systems with matrix factorization and analysis of different methods used for the Netflix Challenge.
- Information Extraction from scanned documents - from rule based to Machine Learning (Florian Kuhlmann, leverton.de)
Abstract hopefully coming soon.
- Meet robots halfway (Michael Bucko, 20min)
Gödel once wondered how we can understand each other. Yes, we all have different definitions of love. I will follow this idea and present defin3.com- the tool, which I build to do something with this issue and meet robots halfway. Defin3.com is meant to be a moderated user-based (Wiki-style) dictionary of (connected) meanings. The rule is: to build a higher abstraction word, you need to have its building blocks defined first. In my talk, I am going to focus on the algorithms behind the quality of claims that can be built from the words defined in defin3.com (http://defin3.com/).
Have an interesting topic you want to talk about?
We're always looking for interesting presentations for our meetups. If you have a topic you're excited about and you want to talk about, maybe between 15 and 50 minutes, then send a short abstract to daniel.nouri gmail.com. Thanks!

#6: recsys with matrix factorization and information extraction from documents