Reading Notes - Unit 12

Ahn et al. - Personalized Web Exploration with Task Models

     This paper is about personalized web search, specifically exploratory web search.   Exploratory searches are those that go beyond the typical "how many inches in a foot" type searches that seek a simple answer.  This paper covers the testing of a tool the authors came up with called TaskSieve.  TaskSieve uses relevance feedback to offer the user personalized search.

Pazzani & Billsus - Content-Based Recommendation Systems

     This paper is about content-based recommendation systems.  These systems are used everyday from web search to Amazon.com as a way to help the customer find other items they may enjoy and/or to help the retailer sell more product.  These systems are usually helped by algorithms that analyze a user's prior history, though sometimes they also have the user enter the information too.

Gauch et al. - User Profiles for Personalized Information Access

     This paper dove tails nicely with the previous two of this week.  It covers the profiling of users.  It covers methods for user identification, and other collection techniques.  The paper looks at the need of companies and projects to have access to more specific information about their customers and participants.  It is interesting that they only briefly touch on the privacy implications of all the interesting facts that you can glean from the user, both implicitly and explicitly.