1. User Profiles for
Personalized Information Access
(1) User Profiling: gather,
and exploit, some information about individuals in order to be effective.
(2) Explicit User
Information Collection: user feedback, customization, navigation
(3) Implicit User
Information Collection: browser caches, proxy servers, browser agents, desktop
agents, and search logs.
2. Content-Based
Recommendation Systems
describing the items that
may be recommended, a means for creating a profile of the user that describes
the types of items the user likes, and a means of comparing items to the user
profile to determine what to recommend
(1)
Item
Representation: Items that can be recommended to the user are often stored in a
database table.
(2)
User Profiles:
A profile of the user’s interests
(3)
Learning a
User Model: Creating a model of the user’s preference from the user history
(4)
Decision
Trees and Rule Induction
(5)
Nearest
Neighbor Methods
(6)
Relevance
Feedback and Rocchio’s Algorithm
(7)
Linear
Classifiers
(8)
Probabilistic
Methods and Naïve Bayes
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