Description:

A probabilistic generative model for hypertext document collections that explicitly models the generation of links. Specifically, links from a word w to a document d depend directly on how frequent the topic of w is in d, in addition to the in-degree of d. We show how to perform

EM learning on this model efficiently. By not modeling links as analogous to words, we end up using far less free parameters, and obtain better link prediction results.

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Linux

Data Formats: None

Tags: Lda, Latent Semantic Analysis, Topic Modeling, Graphical Models, Em

Archive: download here

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