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Tag-Author-User (TAU) : modeling words, tags, authors, users simultaneously

Tag-Author-User (TAU) : modeling words, tags, authors, users simultaneously . Note: we can also model conferences if we want. This is intuitive: users usually assign tags related with conference names such as ICML. Generation Process of D-TAU: .

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Tag-Author-User (TAU) : modeling words, tags, authors, users simultaneously

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  1. Tag-Author-User (TAU) : modeling words, tags, authors, users simultaneously Note: we can also model conferences if we want. This is intuitive: users usually assign tags related with conference names such as ICML.

  2. Generation Process of D-TAU: For each author , draw author-topic distribution from Dir( ) For each user , draw user-topic distribution from Dir( ) For each topic , draw topic-word distribution from Dir( ) For each topic , draw topic-tag distribution from Dir( ) For each tag g , draw a prob value from Beta( ) For each word w ( ) (1) sample uniformly an author from (1) sample a topic from (1) sample w from 3. For each tag g ( ) (1) sample a situation from Binomial ( , 1.0 - ) (2) if s is “generate from text” a. uniformly sample a topic from a. sample g from (3) if s is “generate from user” a. sample uniformly a user from b. sample a topic from c. sample g from

  3. Significance of TAU: No previous models have considered users and authors at the same time. Users and authors are two types of active selector for words or tags. Conferences: Some tags reflect the conference names: icml, sigir 3. When assigning tags, users may check the authors and conferences. Therefore, it makes sense to consider these factors in the generation process of social tags.

  4. Discriminative TAU (D-TAU): represent in a discriminative, rather than generative, manner

  5. Generation Process of D-TAU: For each author , draw author-topic distribution from Dir( ) For each user , draw user-topic distribution from Dir( ) For each topic , draw topic-word distribution from Dir( ) For each topic , draw topic-tag distribution from Dir( ) For each word w ( ) (1) sample uniformly an author from (1) sample a topic from (1) sample w from 3. For each tag g ( ) only consider “generate from user” (1) sample uniformly a user from (2) sample a topic from (3) sample g from

  6. After each updating: To find the parameters w: KL-divergence or JS-divergence Comments: Use the P(g|r) as supervised information to guide the discriminative process. Problem: How can we influence Gibbs Sampling using P(g|r) ?

  7. With parameters estimated, tag prediction is an easy task:

  8. Specifity of a tag: An interesting minor point I would like to incorporate into our tasks. But have not figured out how. The similarity of two resources, can be examined in the space of tags

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