Notes

• Rehearsal Dates: 5/21 (for oral defense)
• Defense Notes:
  傳統的 CF 推薦系統
  是假設具有相似購買行為的使用者會買類似的產品
  在本 tag suggestion 機制
  也是利用類似的想法
  假設相似的paper會有相似的標註

  demographic recommender
  demographic data: e.g. 性別 年齡 職業
  demographic vector: 維度是寫死的 (not flexible)

  Information filtering systems can help users eliminate useless documents and bring to their attention only the relevant information. This implies that the system has to be able to recognize the users and to maintain a model for their interests.

  LifeStyleFinder: demographic recommender system based on profiling user in demographic clusters.
  SiteIF: web page recommender based on keyword matching

  SIFT: a content-based recommender for Netnews recommendations.
  http://wwwis.win.tue.nl/ah98/Stefani/Stefani.html

  Article related to tagging process
  http://en.wikipedia.org/wiki/Tag_%28metadata%29

  Introduction to Collaborative tagging systems
  Systems that allow users to user their created keywords to organize and share content

  Example:
  delicious: bookmarking,
  flicker: photo
  CiteULike: bibliography

  Motivation 1: How to enhance content sharing in tagging community
  1. create tag related to the content
  2. use popular tag

  Problem 1: However, sometimes we have no idea to tag an item we are not familar with.
  => we need tag suggestion mechanisms
  .
  Problem 2: Information overload (content accumulated to massive amount)
  1. too time-consuming to find useful information (explain def. for useful)
  => we need content recommendation mechanisms

  In our work, we want to create a collaborative tagging system for bibliography management. In addition, we proposed tag suggestion and content recommendation mechanisms to solve the problems above. (come with the illustration for problem definition)

• Related work
  1. collaborative tagging system for bibliography management
  - CiteULike, Bibsonomy
  2. recommender system
  - demographic approach
  - content-based approach
  - collaborative-filtering approach

  Data analysis
  - CiteULike dataset
  - analysis in 3 views: user, tag, item (explain for long tail phenomenon)
  - sparsity and user correlation (item vs. tag)
  - conclusion for data analysis

  System components overview and Implementation
  - bib agent (with flow chat built by left)
  - bib management system (can come with database schema)
  - bib search engine (Lemur indexing)
  - tag suggestion engine (explain for CF approach) with evaluation
  - bib recommender (2 simple mechanisms) with evaluation

  Conclusion
  - What I have done in this work