[Seeks-users] Short 'under the hood' Seeks talk at FOSDEM 2011 Data Devroom

[Emmanuel B. <ebe...@se...>]

Hi Seeksers,

FYI our submission to the FOSDEM Data Devroom this year has been accepted.

Therefore there will be a short 15 mins talk on Saturday the 5th at 6pm.

I'll outline the main machine learning and similarity analysis algorithms
that are currently implemented and will give some insights about where
we are heading. This latter part includes possibly more machine learning
for local results personalization and data selection.

Below is the submission as I did send it.

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* Speaker: Emmanuel Benazera

* Biography: Emmanuel Benazera received a master degree in applied mathematics and social sciences
  from University Paris-Dauphine, France in 1999, and Ph.D. degree in computer science
  in 2003 from Paul Sabatier University, France. He was a visiting scientist at the NASA
  Ames Research Center for two years before he joined the robotics department of
  DFKI/University of Bremen in Germany. Then he was momentarily with LAAS-CNRS in Toulouse,
  France. His main research interests include automated decision making & planning, machine
  learning, information retrieval and p2p networks.

* Title: How Seeks let you do your Web search at home:

* Abstract: Seeks is a free and open P2P design and application for enabling social websearch.
 Its specific purpose is to regroup users whose queries are similar so they can share both the
 query results and their experience on these results.

 Seeks is designed to run as a proxy on a user's machine. As such it studies the user's
 behavior and uses these data locally to improve websearch results. Other features include
 automated similarity analysis for text and images, automated clustering of textual content,
 and most discriminant words highlighting.

 Seeks relies on a mixture of machine learning & similarity analysis tools applied
 to Web search and results personalization. These tools are implemented with one goal in mind:
 give more control to the user in its searches. Typically, machine learning and information
 retrieval algorithms help squeeze more information out of the data. However, Seeks always
 let the user decide what matters to him. It does so by studying user clicks and navigation,
 and using this data to make recommendations (results, queries, URLs). This talk intends
 to outline some of these tools, and to show how their careful integration benefit many
 searches.

 In a near future, Seeks instances will talk to each other through a P2P overlay network (DHT),
 so that users can share their experience over similar queries.

* Audience: intermediate

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Happy New Year celebrations,

Em.