1. A Contextual Computing approach towards Personalized Search
Jim Pitkow
August 2002

2. Contextual Computing
The enhancement of a user’s interactions by understanding:
the user,
their context,
and the applications/information being used,
across a wide set of user goals.
Actively adapting the computational environment, for each and every user, at each point of computation
Not just about modeling the user’s preferences and behavior or embedding computation everywhere

3. Evolution: Information Retrieval
Methods of Applying Relevance
Multidimensional Inferencing
User Model Based +
Link Analysis
Popularity
(Google, search engines after 1999/2000)
(DirectHit, most search engines after 1999/2000)
Popularity Based +
Boolean
Vector Space
(Library and legal systems, Lexis-Nexis, West Law)
(Verity, search engine companies till 98)
Content Based
Accessibility of Information/Volume

4. Personal Search Architecture
Search Enterprise
Outride Personalized Search System
Query Augmentation
User Query
Search: Web
Demographics
Click Stream
Commerce: Retail
Result Processing
Result Set
Search History
Retrieval over new spaces (Excite’s)
Provide retrieval from any Web spaces
Contextualization
Leverage the customer’s current behavior to make search and targeted Excite alerts smarter and more relevant in real-time
Personalization
Leverage the customer’s past preferences to individualize results
Application Usage
Commerce: B2B
Contextualized Client Interface
Outride Schema User x Content x History x Demographics
Search Engine Schema Keyword by Doc IDs by Link Rank

6. End-User Benefits: Be More Productive
SOURCE: ZDLabs/eTesting, Inc. October 2000
% Increase from Outride Enabled Search
130.2% more time
93.7% more time
107.9% more time
114.5% more time
Search Engine
Craig
Time in Seconds

7. Novice versus Expert Gains
Average Time to Complete Task
Craig
User Skill Level