1. Discovering User Access Patterns on the World-Wide Web
David W. Cheung, Ben Kao, Joseph Lee

2. Classifying Web Tools Level 0 : Mosaic, Netscape
retrieves documents for a user straight orders
Level 1 : Alta Vista
provides a user-initiated searching facility
Level 2 : WebWatcher, SIFT
maintain user profile
have an active component for notifying users whenever new relevant information is found

3. Level 3 : DiffAgent, Letizia Level 4
a learning and deductive component of user profiles
Level 4
capability of leaning the behavior of both information users and information sources

4. Requirements Discover its users topics of interest automatically
Learn its users access patterns and information sources update patterns
Make efficient use of network resources
Maintain a database and a full-text index on retrie-ved documents
Be compatible with most WWW browsers

5. System Architecture Internet WWW Browser Netscape Suggestion Agent
Search Engine
Learning Agent
Monitor Agent
Document Manager
Proxy Server
Mosaic
Internet
Access Log
Document Database
User Accounts
User Profile

6. Learning Agent
discovers user access patterns and topics of interest by analyzing the access log created by the Proxy
generates a user profile for each user
Topics of interest  Search Engine
Time-related access patterns  Monitor Agent

7. Discovery of Access Patterns
Log Preprocessing
Relevancy Determination
Topics Discovery
User Access Log
Raw Term Vectors
Adjusted Term Vectors
User Topics

8. Phase 1
process each textual document as recorded in the user access log
produce a term vector of (keyword, weght) pairs
ex. (NBA, 50), (basketball, 35)
a modified formula for TFDIF : weight 계산

9. Phase 2 noise in the raw term vectors
ex. Reference page have a large number of reference hyperlinks
use the heuristics to adjust the relevancy of the raw term vectors extracted in the first phase
output : a sequence of term vectors with their weights adjusted

10. Heuristics for Phase 2 the number of hyperlinks
the amount of time that a user has spent in a document
a browse movement graph
a high fan-out number
the functional role in a document
ex. <TITLE>, <H1>
the keywords found in URLs

11. Phase 3 Produce the topics of interests from adjusted term vectors
Clustering technique
output : a small number of topic vectors
ex. (NBA, basketball, stadium, arena)