Adaptive Information Filtering

Abstract

This project studies personalized proactive information filtering agents that pushes relevant information to the user without requiring explicit user query. To do this, the agent adaptively learns a detailed user model while observing and interacting with the user. We use Bayesian statistical theory and machine learning techniques to tackle the following two major challenges. We studied two major problems: how to build an initial user profile with minimal user effort, and How to improve personalized recommendation based on multiple evidences, such as social networks, implicit user feedback, and explicit user feedback and context information. This project led to 1 book chapter, 2 journal paper, 4 conference publications and one demo system.

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Document Details

Document Type
Technical Report
Publication Date
Feb 25, 2011
Accession Number
ADA563638

Entities

People

  • Yi Zhang

Organizations

  • University of California, Santa Cruz

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Data Mining
  • Data Sets
  • Electronic Commerce
  • Feedback
  • Filtration
  • Information Processing
  • Information Retrieval
  • Information Science
  • Knowledge Management
  • Learning
  • Machine Learning
  • Online Communications
  • Security
  • Social Media
  • Social Networks
  • Standards

Fields of Study

  • Computer science

Readers

  • Database Systems and Applications
  • Library and Information Science
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval