Latent dirichlet allocation
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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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Proceedings Article
Short and sparse text topic modeling via self-aggregation
TL;DR: A novel model integrating topic modeling with short text aggregation during topic inference is presented, founded on general topical affinity of texts rather than particular heuristics, making the model readily applicable to various short texts.
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Text Analysis in R
TL;DR: This teacher’s corner provides an overview of general steps and operations in a computational text analysis project, and demonstrates how each step can be performed using the R statistical software.
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Enhancing Collaborative Filtering by User Interest Expansion via Personalized Ranking
TL;DR: The iExpand method introduces a three-layer, user-interests-item, representation scheme, which leads to more accurate ranking recommendation results with less computation cost and helps the understanding of the interactions among users, items, and user interests.
Proceedings ArticleDOI
Context-Aware Event Recommendation in Event-based Social Networks
TL;DR: This work proposes to exploit several contextual signals available from EBSNs to exploitSeveral contextual signals for learning to rank events for personalized recommendation and demonstrates the effectiveness of the proposed contextual learning approach in contrast to state-of-the-art event recommenders from the literature.
Journal ArticleDOI
Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging.
Ani Eloyan,John Muschelli,John Muschelli,Mary Beth Nebel,Han Liu,Fang Han,Tuo Zhao,Anita D. Barber,Suresh Joel,James J. Pekar,Stewart H. Mostofsky,Brian Caffo +11 more
TL;DR: A prediction model is created using the landmark ADHD 200 data set focusing on resting state functional connectivity (rs-fc) and structural brain imaging and the most promising imaging biomarker was a correlation graph from a motor network parcellation.
References
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Indexing by Latent Semantic Analysis
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