Latent dirichlet allocation

doi:10.5555/944919.944937

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Latent dirichlet allocation

Journal Article•DOI•

Latent dirichlet allocation

David M. Blei¹, Andrew Y. Ng², Michael I. Jordan¹•Institutions (2)

University of California, Berkeley¹, Stanford University²

01 Mar 2003-Journal of Machine Learning Research (JMLR.org)-Vol. 3, pp 993-1022

TL;DR: 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.

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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.

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Journal Article•DOI•

Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges

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Yue Shi¹, Martha Larson¹, Alan Hanjalic¹•Institutions (1)

Delft University of Technology¹

01 May 2014-ACM Computing Surveys

TL;DR: A comprehensive introduction to a large body of research, more than 200 key references, is provided, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix.

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Abstract: Over the past two decades, a large amount of research effort has been devoted to developing algorithms that generate recommendations. The resulting research progress has established the importance of the user-item (U-I) matrix, which encodes the individual preferences of users for items in a collection, for recommender systems. The U-I matrix provides the basis for collaborative filtering (CF) techniques, the dominant framework for recommender systems. Currently, new recommendation scenarios are emerging that offer promising new information that goes beyond the U-I matrix. This information can be divided into two categories related to its source: rich side information concerning users and items, and interaction information associated with the interplay of users and items. In this survey, we summarize and analyze recommendation scenarios involving information sources and the CF algorithms that have been recently developed to address them. We provide a comprehensive introduction to a large body of research, more than 200 key references, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix. On the basis of this material, we identify and discuss what we see as the central challenges lying ahead for recommender system technology, both in terms of extensions of existing techniques as well as of the integration of techniques and technologies drawn from other research areas.

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777 citations

Cites background or methods from "Latent dirichlet allocation"

...Based on the framework of regression-based latent factor models, social trust relationship of users has been incorporated into MF for improving the learning of latent user factors and, as a result, improving the recommendation performance [Jamali and Ester 2010; Ma et al. 2009]. fLDA [Agarwal and Chen 2010] can be regarded as a specific extension of regressionbased latent factor models that targets the recommendation scenarios with rich side information. fLDA makes use of latent Dirichlet allocation (LDA) [Blei et al. 2003] to regularize a factorization model and suits to the cases in which side information can be represented in the form of a “bag of words” (i.e., with statistics of the occurrences of individual words). fLDA fits the relevance scores in the U-I matrix based on the regression predictor against user features and the regression predictor of item features, in combination with the inner product of the user’s interest in the latent topics and the degrees of latent topics estimated from item metadata. fLDA and the work of Porteous et al. [2010] are similar to each other with respect to the use of an ensemble....
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...…latent factor models that targets the recommendation scenarios with rich side information. fLDA makes use of latent Dirichlet allocation (LDA) [Blei et al. 2003] to regularize a factorization model and suits to the cases in which side information can be represented in the form of a “bag of…...
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...In the work of Porteous et al. [2010], this interaction is incorporated directly during MF, whereas fLDA incorporates it through LDA [Agarwal and Chen 2010]....
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...fLDA makes use of latent Dirichlet allocation (LDA) [Blei et al. 2003] to regularize a factorization model and suits to the cases in which side information can be represented in the form of a “bag of words” (i....
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Proceedings Article•

Infinite latent feature models and the Indian buffet process

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Zoubin Ghahramani¹, Thomas L. Griffiths²•Institutions (2)

University College London¹, Brown University²

05 Dec 2005

TL;DR: A probability distribution over equivalence classes of binary matrices with a finite number of rows and an unbounded number of columns is defined, suitable for use as a prior in probabilistic models that represent objects using a potentially infinite array of features.

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Abstract: We define a probability distribution over equivalence classes of binary matrices with a finite number of rows and an unbounded number of columns. This distribution is suitable for use as a prior in probabilistic models that represent objects using a potentially infinite array of features. We identify a simple generative process that results in the same distribution over equivalence classes, which we call the Indian buffet process. We illustrate the use of this distribution as a prior in an infinite latent feature model, deriving a Markov chain Monte Carlo algorithm for inference in this model and applying the algorithm to an image dataset.

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776 citations

Cites methods from "Latent dirichlet allocation"

...This approach has proven successful in modeling the content of documents, where each feature indicates one of the topics that appears in the document (e.g., Blei, Ng, & Jordan, 2003 )....
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Journal Article•DOI•

stm: An R Package for Structural Topic Models

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Margaret E. Roberts¹, Brandon M. Stewart², Brandon M. Stewart³, Dustin Tingley⁴•Institutions (4)

University of California, San Diego¹, Princeton University², Center for Information Technology³, Harvard University⁴

31 Oct 2019-Journal of Statistical Software

TL;DR: This paper demonstrates how to use the R package stm for structural topic modeling, which allows researchers to flexibly estimate a topic model that includes document-level metadata.

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Abstract: This paper demonstrates how to use the R package stm for structural topic modeling. The structural topic model allows researchers to flexibly estimate a topic model that includes document-level metadata. Estimation is accomplished through a fast variational approximation. The stm package provides many useful features, including rich ways to explore topics, estimate uncertainty, and visualize quantities of interest.

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771 citations

Cites background or methods from "Latent dirichlet allocation"

...It will, however, always produce the same result within a machine for a given version of stm. 11Starting with version 1.3.0 the default initialization strategy was changed to Spectral from LDA. can be passed to selectModels, such as max.em.its....
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...1 Building off of the tradition of probabilistic topic models, such as the Latent Dirichlet Allocation (LDA) (Blei et al. 2003), the Correlated Topic Model (CTM) (Blei and Lafferty 2007), and other topic models that have (1)We thank Antonio Coppola, Jetson Leder-Luis, Christopher Lucas, and Alex Storer for various assistance in the construction of this package....
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...…reading of text corpora.1 Building off of the tradition of probabilistic topic models, such as the Latent Dirichlet Allocation (LDA) (Blei et al. 2003), the Correlated Topic Model (CTM) (Blei and Lafferty 2007), and other topic models that have 1We thank Antonio Coppola, Jetson…...
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...Keywords: structural topic model, text analysis, LDA, stm, R....
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...The second approach is to initialize the model with a short run of a collapsed Gibbs sampler for LDA....
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Proceedings Article•

Vector-based Models of Semantic Composition

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Jeff Mitchell¹, Mirella Lapata¹•Institutions (1)

University of Edinburgh¹

01 Jun 2008

TL;DR: Under this framework, a wide range of composition models are introduced which are evaluated empirically on a sentence similarity task and demonstrate that the multiplicative models are superior to the additive alternatives when compared against human judgments.

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Abstract: This paper proposes a framework for representing the meaning of phrases and sentences in vector space. Central to our approach is vector composition which we operationalize in terms of additive and multiplicative functions. Under this framework, we introduce a wide range of composition models which we evaluate empirically on a sentence similarity task. Experimental results demonstrate that the multiplicative models are superior to the additive alternatives when compared against human judgments.

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770 citations

Proceedings Article•

Characterizing Microblogs with Topic Models

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Daniel Ramage¹, Susan T. Dumais², Daniel J. Liebling²•Institutions (2)

Stanford University¹, Microsoft²

16 May 2010

TL;DR: A scalable implementation of a partially supervised learning model (Labeled LDA) that maps the content of the Twitter feed into dimensions that correspond roughly to substance, style, status, and social characteristics of posts is presented.

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Abstract: As microblogging grows in popularity, services like Twitter are coming to support information gathering needs above and beyond their traditional roles as social networks. But most users’ interaction with Twitter is still primarily focused on their social graphs, forcing the often inappropriate conflation of “people I follow” with “stuff I want to read.” We characterize some information needs that the current Twitter interface fails to support, and argue for better representations of content for solving these challenges. We present a scalable implementation of a partially supervised learning model (Labeled LDA) that maps the content of the Twitter feed into dimensions. These dimensions correspond roughly to substance, style, status, and social characteristics of posts. We characterize users and tweets using this model, and present results on two information consumption oriented tasks.

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764 citations

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References

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Book•

Handbook of mathematical functions : with formulas, graphs, and mathematical tables

[...]

Milton Abramowitz, Irene A. Stegun

01 Jan 1970

17,608 citations

Book•

Bayesian Data Analysis

[...]

Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin¹ - Show less +2 more•Institutions (1)

University of California, Irvine¹

01 Jan 1995

TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.

...read moreread less

Abstract: FUNDAMENTALS OF BAYESIAN INFERENCE Probability and Inference Single-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian Approaches Hierarchical Models FUNDAMENTALS OF BAYESIAN DATA ANALYSIS Model Checking Evaluating, Comparing, and Expanding Models Modeling Accounting for Data Collection Decision Analysis ADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional Approximations REGRESSION MODELS Introduction to Regression Models Hierarchical Linear Models Generalized Linear Models Models for Robust Inference Models for Missing Data NONLINEAR AND NONPARAMETRIC MODELS Parametric Nonlinear Models Basic Function Models Gaussian Process Models Finite Mixture Models Dirichlet Process Models APPENDICES A: Standard Probability Distributions B: Outline of Proofs of Asymptotic Theorems C: Computation in R and Stan Bibliographic Notes and Exercises appear at the end of each chapter.

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16,079 citations

"Latent dirichlet allocation" refers background in this paper

...Finally, Griffiths and Steyvers (2002) have presented a Markov chain Monte Carlo algorithm for LDA....
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...Structures similar to that shown in Figure 1 are often studied in Bayesian statistical modeling, where they are referred to ashierarchical models(Gelman et al., 1995), or more precisely asconditionally independent hierarchical models(Kass and Steffey, 1989)....
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...Structures similar to that shown in Figure 1 are often studied in Bayesian statistical modeling, where they are referred to as hierarchical models (Gelman et al., 1995), or more precisely as conditionally independent hierarchical models (Kass and Steffey, 1989)....
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Journal Article•DOI•

Indexing by Latent Semantic Analysis

[...]

Scott Deerwester¹, Susan T. Dumais², George W. Furnas², Thomas K. Landauer², Richard A. Harshman³ - Show less +1 more•Institutions (3)

University of Chicago¹, Telcordia Technologies², University of Western Ontario³

01 Sep 1990-Journal of the Association for Information Science and Technology

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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Abstract: A new method for automatic indexing and retrieval is described. The approach is 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. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. initial tests find this completely automatic method for retrieval to be promising.

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12,443 citations

"Latent dirichlet allocation" refers methods in this paper

...To address these shortcomings, IR researchers have proposed several other dimensionality reduction techniques, most notably latent semantic indexing (LSI) (Deerwester et al., 1990)....
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...To address these shortcomings, IR researchers have proposed several other dimensionality reduction techniques, most notablylatent semantic indexing (LSI)(Deerwester et al., 1990)....
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Book•

Introduction to Modern Information Retrieval

[...]

Gerard Salton, Michael J. McGill

01 Jan 1983

TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.

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Abstract: Some people may be laughing when looking at you reading in your spare time. Some may be admired of you. And some may want be like you who have reading hobby. What about your own feel? Have you felt right? Reading is a need and a hobby at once. This condition is the on that will make you feel that you must read. If you know are looking for the book enPDFd introduction to modern information retrieval as the choice of reading, you can find here.

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12,059 citations

"Latent dirichlet allocation" refers background or methods in this paper

...In the populartf-idf scheme (Salton and McGill, 1983), a basic vocabulary of “words” or “terms” is chosen, and, for each document in the corpus, a count is formed of the number of occurrences of each word....
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...We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model....
[...]

Book•

Theory of probability

[...]

Harold Jeffreys, R. Bruce Lindsay

01 Jan 1939

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.

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Abstract: 1. Fundamental notions 2. Direct probabilities 3. Estimation problems 4. Approximate methods and simplifications 5. Significance tests: one new parameter 6. Significance tests: various complications 7. Frequency definitions and direct methods 8. General questions

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7,086 citations