Collaborative Filtering:

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Collaborative Filtering
Searching and Retrieving Web Information Together
Huimin Lu December 2, 2004 INF 385D Fall
2004 Instructor Don Turnbull
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Outline

• Introduction
• Collaborative Search Family
• Collaborative Filtering
• Systems
• Process
• Algorithm
• Problems Solutions
• Privacy

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Collaborative Search into IR World

• Inverted Index
• Yellow-pages-like information gateway
• Internet search engine (Sun,
  1999)
• Needs for collaborative retrieval
• Information-resources-focused systems
• - By CSCW structuring mechanisms
• recommendation
  techniques
• User-preferences-focused systems

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Collaborative Search Types

• Collaborative browsing
• Mediated searching
• Collaborative information filtering
• Collaborative agents
• - meda-search engines
• Collaborative re-use of results
• (Setten,
  2000)

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Collaborative Filtering

• User-based filtering
• Collects the taste information from users
  
• who like to collaborate in the process of
  searching and automatically predict or filter
  the relevant information to users (Wikipedia,
  2004).
• Store profile preferences
• Build users database
• Recommended list by collaborative filter
  

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Collaborative Filtering Systems

• Commercial
• - Amazon
• - Barnes and Noble
• - Netflix
• Non-commercial
• - Moonranker
• - MovieLens
• - AmphetaRate
• - Audioscrobbler
• - Findory
• - Gnomoradio
• - iRATE radio

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System Example I Amazon.com Recommendation page
Back
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System Example II Moonranker.com ranking page
Back
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System Example I Movielens.com rating page
Back
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Collaborative Filtering Process
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Collaborative Filtering Algorithm

• Goal
• - Suggest new items/predict the utility
  based on previous likings (Sarwar, 2001)
• Memory-based
• - use entire user-item database
• - Pearson-correlation based approach, vector
  similarity based approach, the extended
  generalized vector space model
• Model-based
• - develop a model of user rating
• - Bayesian network approach, the aspect model

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Problems and Solutions

• Memory-based algorithm problems
• - Sparsity insufficient user rating
  information
• - Scalability nearest neighbor algorithm
  (compute user number and item number)
• - Solution automatic weighting scheme by
  MSU CMU
• Model-based algorithm problem
• - Inherent static structure updating
  problem learning exact cluster number and
  specifying user classes problem
• Systems problems
• - Scarcity less rating for some items
• - Early-rater no recommendations for new
  items
• - Solution collaborative information
  filtering (communicating agents, correlating
  profile, and filterbots - automated rating
  robots)

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Privacy

• Unsafe server-based system
• Monopolies
• Peer-to-peer architecture
• - Multi-party computation

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Conclusion
The computer environment turns to be more
ubiquitous and pervasive. To meet IR users
needs, future collaborative filtering system
should be easily maintained with well-designed
algorithms and highly-protected user privacy.
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References
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References
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References
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Questions or Comments?