Automatic Evaluation Of Search Engines Project Poster Team members: Levin Boris Laserson Itamar Instructor Name: Gurevich Maxim

Introduction Search engines have become a very popular and important tool How can we compare different search engines? Coming up with a set of tests which are absolute for all the engines. One way - Randomly sampling results from search engines and then comparing them. In this project we will implement two algorithms - the Metropolis-Hastings (MH) and the Maximum Degree algorithm (MD) for doing just that.

Background Bharat and Broder proposed a simple algorithm for uniformly sampling documents from a search. The algorithm formulates “random” queries, submits and picks uniformly chosen documents from the result sets. We present another sampler, the random walk sampler This sampler performs a random walk on a virtual graph defined over the documents. The algorithm first produces biased samples - some documents are more likely to be sampled than others. The two algorithms we implement come to fix this bias issue.

Basic Random Walk description netscape.com amazon.com

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