Workshop on Subspace, Latent Structure and Feature Selection Techniques: Statistical and Optimisation Perspectives, Bohinj 2005

Latent Semantic Variable Models

author: Thomas Hofmann, Google, Inc.
published: Feb. 25, 2007, recorded: February 2005, views: 29649

Slides

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Description

In the context of information retrieval and natural language processing, latent variable models are quite useful in modeling and discovering hidden structure that often leads to "semantic" data representations. This talk will provide an overview of the most popular approaches and discuss the range of possible applications for such models, including language modeling, ad hoc retrieval, text categorization and collaborative filtering.

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Reviews and comments:

1 Mike, March 12, 2008 at 1:18 p.m.:

Interesting talk, too bad the last part about recommenders is missing.

2 R., May 23, 2008 at 8:18 p.m.:

On the first EM slide, should the bottom right equation be P(d|z; pi) instead of P(z|d; pi)?

3 Andrew Polar, October 6, 2011 at 7:24 a.m.:

Thomas Hufmann over-complicates real nature of his algorithm on purpose. It can be perfectly clear explained in linear algebra notations. Linear algebra excludes obscurity. Here is the link where everything is explained on 1 page:
http://semanticsearchart.com/research...

4 Kiran, July 5, 2020 at 4:23 p.m.:

Hey where is the last part of recommenders. Please upload it soon, it will be really good
https://dgcustomerfirstcom.org

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