Posts Tagged ‘Yahoo’
The american dream awaits.
Silicon Valley, here I come!
I will be traveling to USA for the WSDM’12 conference to present our work on news recommendation: “From Chatter to Headlines: Harnessing the Real-Time Web for Personalized News Recommendation”. T.Rex (Twitter based RECommendation System) blends signals from social circles, personal interests and popularity to learn a personalized ranking function for online news articles. A more detailed explanation will follow as an invited post on another blog.
At WSDM I will be giving a 4 minutes spotlight presentation and then I will explain the details in a long poster session.
Then, I will fly to California to present T.Rex at the annual Yahoo! Labs meeting: Science Week. There I will have a 10 minutes slot in a workshop about news experiments and a 1 minute presentation in a fun competition called “60 seconds of science”.
It’s going to be challenging to present the same topic in so many different formats!
At Yahoo! Research, you need a Ph.D. to get a cubicle.
With a Master’s you get at most a small desk!
My last work “Social Content Matching in MapReduce” got accepted in VLDB
(Very Large Data Bases).
(as you might tell, I am extremely happy about this 🙂 )
In the paper we tackle the problem of content distribution in a social media web site like flickr, model the problem as a b-matching problem on a graph and solve it with a smart iterative algorithm in MapReduce. We also show how to design a scalable greedy algorithm for the same problem in MapReduce.
Here the abstract:
Matching problems are ubiquitous. They occur in economic markets, labor markets, internet advertising, and elsewhere. In this paper we focus on an application of matching for social media. Our goal is to distribute content from information suppliers to information consumers.
We seek to maximize the overall relevance of the matched content from suppliers to consumers while regulating the overall activity, e.g., ensuring that no consumer is overwhelmed with data and that all suppliers have chances to deliver their content.
We propose two matching algorithms, GreedyMR and StackMR, geared for the MapReduce paradigm. Both algorithms have provable approximation guarantees, and in practice they produce high-quality solutions. While both algorithms scale extremely well, we can show that StackMR requires only a poly-logarithmic number of MapReduce steps, making it an attractive option for applications with very large datasets. We experimentally show the trade-offs between quality and efficiency of our solutions on two large datasets coming from real-world social-media web sites.
On a final note, thanks to my co-authors for their hard work and guidance:
Aris Gionis from Yahoo! Research and Mauro Sozio from Max Planck Institut.
From 20 Sep 2010 to 31 Mar 2011 I will be visiting Yahoo! Research Labs in Barcelona, Spain as a “Research Intern”.
I am really glad for this opportunity to work in a thriving environment and live in this wonderful city. I already love the place.
I will be still working on my thesis during these 6 months, but I will probably (and hopefully) open up new research paths. I will also try to continue my work on Apache Pig, as it is widely used inside Yahoo!