A case for Intent Motivated Search Technique

1. A case for Intent Motivated Search Technique Presented By: Raymond Gao Wednesday, January 02, 2008

2. Presentation Roadmap • Why is relevant Search experience difficult to provide? • The evolution of Search Engine • My demo (validating Intent Motivated Search Technique). • Discussing my application – Technical Aspect.

3. Opening Question • What is a common feature provided by website A-Z, from Amazon, to Ebay, to Google, to Zillow.com? • Search is everywhere

4. An illustration of un-intended search results? • Query 1a - Where to buy an Amazon book? • Top results are on Amazon.com (Not what I intended. I want books about the Amazon Jungle.)

5. Query 1b - Where to get an Amazon book free?

6. Query 1c - I want to visit Amazon area, need free info.

7. Illustration #2 • Query 2a - Where to buy Mozart Kugel in Salzburg? • Results are on Cholocate. (Not my 1st choice. I want to find a store in Salzburg to buy cheap Mozart Kugel.)

8. What did I just show? • When a word has multiple meanings, the Search Engine does not know what is intended context. Thus, it fails to return the correct result.

9. AI & Beyond Search by Reference Search by Text Search by Meta-Data Personalized Search The Evolution of Search Engine Intent Motivated Search Relevance of the result Complexity of the Search Engine

10. The Search Evolution • Stage I – Search by Matching Text – Altavista, Excite, Yahoo (pre 2000) • Stage II – Search by Reference – Google, Ask.com • Stage III – Search by Meta-data – Metacrawler, Ice Rocket • Stage IV – Personalized Search – iGoogle, • Stage V – Intent Motivated Search– Mindset.yahoo.com • Stage VI - AISearch Engine & beyond

11. What, How, Which • What I have built? • I have built a search engine front-end that validates the concept of Intent Motivated Search Experience • How is it built? • Aqualogic Portal’s Search Engine • Rule Engine • AJAX and custom code • Which data sources? • See next page

12. Data Sources • Regular files, • Web pages, • Other Portals – M.S. SharePoint, • Group Wares - Lotus Notes, • Other Content Management Systems (Documentum, Vignette, …)

13. Intent Motivated Search (Demo) • User Profiling (Rule drive) • + Meta-Tagging • + User Dictionary • = A Prototype for Intent Motivated Search Experience • User’s search query is affected by his/her group membership, previous experience, &many other factor. • Proposed Solution: an search engine that understands the user’s vocabulary. • Extend the reach of meta-tags, user custom dictionary, with the aid of user profiling.

14. Business Logic • Student has little money, Wikipedia is free • Teacher wants accurate content, & library books form the basis for researched • Amazon.com Reward Member has $50 credit. He prefers to redeem the credit for free items. • (Default) General public has a $25 spending limit. Which source does not matter.

15. Query Process Flow Weighted Query Profiling Interpret Student Teacher Decision Profile Gift Card Holder General Public

16. Example Business Rule

17. Pseudo Code

18. The Benefit of Applying Rule Engine • Business Rule are stored in XML format -> facilitating easy editing; • The Rule Engine can be dynamically executed and used to weight the returned search result; • Business Rule can be readjusted independent of the search indexing engine.

19. Intent & Action • Distinction between Spoken vs. Unspoken intent. • Spoken Intent is from the original query string. • Unspoken Intent maybe inferred from the user attributes, e.g. group membership, profession, age, gender, … • If 80% of Actions are caused by an Intent, by capturing user’s Intention, the Search Experience could improve.

20. Reference • http://www.seomoz.org/blog/segmenting-search-intent • http://Igoogle.com • http://mindset.yahoo.com • http://www.marketingvox.com/archives/2005/05/31/yahoo_mindset_search_results_reflect_searcher_intent/

21. My Contact Information Email address: raygao@verizon.net rgao@bea.com Mobile Phone: 972-765-4423