Latent dirichlet allocation
TLDR
This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.Abstract:
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.read more
Citations
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Journal ArticleDOI
A survey of collaborative filtering techniques
TL;DR: From basic techniques to the state-of-the-art, this paper attempts to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.
Proceedings ArticleDOI
Collaborative Filtering for Implicit Feedback Datasets
TL;DR: This work identifies unique properties of implicit feedback datasets and proposes treating the data as indication of positive and negative preference associated with vastly varying confidence levels, which leads to a factor model which is especially tailored for implicit feedback recommenders.
Journal ArticleDOI
From frequency to meaning: vector space models of semantics
Peter D. Turney,Patrick Pantel +1 more
TL;DR: The goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs, and to provide pointers into the literature for those who are less familiar with the field.
Proceedings ArticleDOI
The political blogosphere and the 2004 U.S. election: divided they blog
Lada A. Adamic,Natalie S. Glance +1 more
TL;DR: Differences in the behavior of liberal and conservative blogs are found, with conservative blogs linking to each other more frequently and in a denser pattern.
Data Mining: Concepts and Techniques (2nd edition)
Jiawei Han,Micheline Kamber +1 more
TL;DR: There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99].
References
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Bayesian Data Analysis
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Journal ArticleDOI
Indexing by Latent Semantic Analysis
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
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Introduction to Modern Information Retrieval
Gerard Salton,Michael J. McGill +1 more
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Harold Jeffreys,R. Bruce Lindsay +1 more
TL;DR: In this paper, the authors introduce the concept of direct probabilities, approximate methods and simplifications, and significant importance tests for various complications, including one new parameter, and various complications for frequency definitions and direct methods.