Specialized search engines

1. Specialized search engines Alex Kotov (04/05/2007)

2. Outline • Vertical search engines; • Opinion search engines; • Personalized search engines; • Social search engines.

3. Vertical search engines • Known as “specialized” search, which addresses the particular information needs of niche audiences and professions (doctors, job seekers, house buyers, recruiters, etc.); • Deliver to users the information that the broad-based search engines can’t without the use of complex keyword combinations;

4. Vertical search design models • Vertical search engine as a separate portal: gizmocafe.com (consumer electronics), loupeit.net (jewelers); • Vertical search engine as a complimentary web site application (embedding a search engine box on an existing site): amazon.com; • Parametric search (allows for face-to-face product and manufacturer comparison): autotrader.com, trulia.com (real estate);

5. Vertical Search Engines • Job search engines (monster.com, hotjobs.com, simplyhired.com); • Medical search engines (gopubmed.co, webmd.com); • Property search engines (rightmove.com, zillow.com); • Accountancy search engines (ifacnet.com); • Legal search engines (westlaw.com, quicklaw.com, lexis.com); • Code search engines (krugle.com, koders.com, Google Code Search); • Comparison shopping search engines (froogle.com, msn shopping, shopzilla, nextag.com, pricerunner.com).

6. Krugle • Allows programmers to search Open Source repositories in order to locate source code and quickly share code with others; • Searches Apache, JavaDocs, SourceForge and Wikipedia amongst other sources; • Plugins for Firefox and Explorer are available.

7. Krugle

8. Krugle

9. Koders • Web-based code search engine + plug-ins for the Eclipse and Microsoft Visual Studio IDEs; • Can be deployed as a stand-alone application on a developer’s desktop or as a networked solution across multiple development teams.

10. Koders

11. Koders

12. Outline • Vertical search engines; • Opinion search engines; • Personalized search engines; • Social search engines.

13. Opinion search engines • Unsupervised information extraction systems, which mine product review data for important product features; • Identify opinions regarding product features and establish their polarity (positive or negative); • Rank opinions based on their strength; • Example: Opine (joint project of Google and U of Washington).

14. Opine

15. Opine

16. Outline • Vertical search engines; • Opinion search engines; • Personalized search engines; • Social search engines.

17. Personalized search engines • Use information about the user to provide better search results; • Require a user to set up a profile; • Filter search results to the user’s area of interest (subject-based personalization); • Analyze and score pages by their attributes, looking for particular categories (attribute-based personalization); • A9 (keeps track of searches and allows to repeat same searches at a later time).

18. Subject-based personalization • Example: Google Personalized Search 1.0; • Users creates a profile by selecting particular categories he is interested in (movies, radio, music); • By using a slider, a user can “personalize” search results to skew them toward particular interest areas; • Pages are classified by topics on the server side.

19. Yahoo MyWeb

20. Google personalized search 2.0

21. Outline • Vertical search engines; • Opinion search engines; • Personalized search engines; • Social search engines.

22. Personalized Search vs. Social Search • Personalized search: by knowing some things about you, a search engine might refine your results to make them more relevant; • Social Search: provide personalized results based not only on who you are, but also on who you know; • Social Search Engines are also called swikis = search engine + wiki.

23. Social Search • Is considered the 3rd big evolution of the search business after algorithmic search and paid search models; • Web 2.0 trends converge toward social search (social networking, consumer generated media, open platforms); • It is about helping people find stuff.

24. Social search • Typical scenario: if you search for “jaguar” and any of your friends have done the same search before, their selections are pushed up in the results page; • Users can see queries posed by their colleagues and help them find necessary information; • Users can see what is “hot topic” for a particular category of users; • Users may opt not to share some queries.

25. Social search • Advantages: • leverage a network of trusted individuals in judging the relevance of search results; • reduced impact of link spam by relying less on the link structure of Web pages; • as opposed to PageRank, Web pages are considered to be relevant from the reader’s perspective, rather than the author’s, who desires their content to be viewed; • Downsides: • as social network grows, commonalities that are useful get diluted. Solution: divide friends into groups; • risk of search spam (user pushing up certain pages in search results). Requires the ability to detect the validity of a users’ contribution; • Potential uses: research group/lab, library.

26. Social search engines • Eurekster; • Yahoo! MyWeb; • Google Coop; • Younanimous; • Decipho; • Rollyo; • Wink (people search).

27. Eurekster • Launched to the public in 2004; • Pioneered vertical, social search; • Is built on top of AllTheWeb search engine; • Hosts 50,000 swickis; • Receives 20 million searches per month or around 500,000 searches per day; • In January 2007 announced one of the 100 best companies in terms of innovation and market potential.

28. Eurekster