Search Engine-Crawler Symbiosis: Adapting to Community InterestsGautam Pant*, Shannon Bradshaw* and Filippo Menczer**

*Department of Management SciencesThe University of Iowa, Iowa City, IA 52246

**School of InformaticsIndiana University, Bloomington, IN 47408

Overview Search Engines and Crawlers The Symbiotic Model Implementation Simulation Study Results

Modern Search Engines

Crawlers

Page Repository(Collection)

Indexer

Text Structure

QueryEngine

Ranking

Indexes

User

QueriesResults

Web(adapted from Searching the Web, Arasu et. al., ACM TOIT 2002)

Search Engine and Crawler Dynamism of the Web Exhaustive crawling Focused needs of a community Topical crawling Freshness, Efficiency, Focus Finding the “right” collection Adapting to drifting interests

Symbiotic Model – High Level

Symbiotic Model - Updating Approach

Implementation Search Engine - Rosetta

RDI - Indexing based on contextual information Voting mechanism

Topical Crawler – Naïve Best-First Frontier as a priority queue Similarity of parent page to the query

qp

qpqpsim

),(

Simulation Study DMOZ “Business/E-Commerce” category Assumption: Interests of the simulated community

lie within the selected category and its sub-categories

Random subset of URLs from categories – bookmark URLs

Database of queries – automatically identify phrases from description of the URLs – filter them manually

Simulation Simulated 5 days of operation Initial collection created through a breadth-first

crawl of 100,000 pages starting from the bookmark URLs

100 queries picked at random from query database for each day

1Gz Pentium III IBM Thinkpad running Windows 2000

Less than 11 hours to build and index a new collection for the next time period

Performance Metrics Collection Quality

Precision@10 Manual evaluation of query results – human subjects made

aware of the context through DMOZ category page

Cp qp

qp

C ˆ

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Results

Results

Results

Related Work Vertical Portals Context based classification, clustering and

indexing Topical or Focused crawlers Collaborative Filtering

Conclusion A model for adaptive vertical portals through tight

coupling of a topical crawler and a search engine Eliminates irrelevant information in short time to

focus on the community interests efficiently Future work

Use of more global information available to a search engine during the crawl

Distribution of symbiotic model to a P2P network

Thank You

Acknowledgements:

Padmini Srinivasan

Kristian Hammod

Rik Belew

Student Volunteers

NSF grant to FM