Generating Predictive Movie Recommendations from Trust in Social Networks

Abstract

Social networks are growing in number and size, with hundreds of millions of user accounts among them. One added benefit of these networks is that they allow users to encode more information about their relationships than just stating who they know. In this work, the authors are particularly interested in trust relationships, and how they can be used in designing interfaces. In this paper, they present FilmTrust, a web site that uses trust in web-based social networks to create predictive movie recommendations. Using the FilmTrust system as a foundation, they show that these recommendations are more accurate than other techniques when the user's opinions about a film are divergent from the average. They discuss this technique both as an application of social network analysis and how it suggests other analyses that can be performed to help improve collaborative filtering algorithms of all types.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA447900

Entities

People

  • Jennifer Golbeck

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Algorithms
  • Computations
  • Filtration
  • Geospatial Intelligence
  • Information Operations
  • Maryland
  • Military Research
  • Networks
  • Online Communities
  • Pilot Studies
  • Ratings
  • Security
  • Social Networks
  • Statistical Sampling
  • Universities

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Systems Analysis and Design