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

2. 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.

3. 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.

4. Basic Random Walk description netscape.com 1 2 3 1 amazon.com 4 0 2 3 0 3 2 2 4 4 3 3 3 1 2 5

5. Results

6. Results