Add to ORKGIn this work, we compare two extensions of two different topic models for the same problem of recommending full-Text items: previously developed SVD-LDA and its counterpart SVD-ARTM based on additive regularization. We show that ARTM naturally leads to the inference algorithm that has to be painstakingly developed for LDA
Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal wit...
Probabilistic topic models, such as LDA, are standard text analysis algorithms that provide predicti...
In this paper, we present preliminary work on identifying equivalent structures required by LDA, and...
In this work, we compare two extensions of two different topic models for the same problem of recomm...
In this work, we compare two extensions of two different topic models for the same problem of recomm...
© Springer International Publishing Switzerland 2015. In recommender systems, matrix decompositions,...
© Springer International Publishing Switzerland 2015. In recommender systems, matrix decompositions,...
© Springer International Publishing Switzerland 2015. In recommender systems, matrix decompositions,...
© Springer International Publishing Switzerland 2015. In recommender systems, matrix decompositions,...
Abstract: Text data has always accounted for a major portion of the world’s information. As the volu...
The probabilistic topic model imposes a low-rank structure on the expectation of the corpus matrix. ...
Abstract—Electronic documents on the Internet are always generated with many kinds of side informati...
Abstract Background Identifying relevant studies for inclusion in a systematic review (i.e. screenin...
Text mining has a wide range of applications in education. In this paper, we review Latent Dirichlet...
Text mining has a wide range of applications in education. In this paper, we review Latent Dirichlet...
Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal wit...
Probabilistic topic models, such as LDA, are standard text analysis algorithms that provide predicti...
In this paper, we present preliminary work on identifying equivalent structures required by LDA, and...
In this work, we compare two extensions of two different topic models for the same problem of recomm...
In this work, we compare two extensions of two different topic models for the same problem of recomm...
© Springer International Publishing Switzerland 2015. In recommender systems, matrix decompositions,...
© Springer International Publishing Switzerland 2015. In recommender systems, matrix decompositions,...
© Springer International Publishing Switzerland 2015. In recommender systems, matrix decompositions,...
© Springer International Publishing Switzerland 2015. In recommender systems, matrix decompositions,...
Abstract: Text data has always accounted for a major portion of the world’s information. As the volu...
The probabilistic topic model imposes a low-rank structure on the expectation of the corpus matrix. ...
Abstract—Electronic documents on the Internet are always generated with many kinds of side informati...
Abstract Background Identifying relevant studies for inclusion in a systematic review (i.e. screenin...
Text mining has a wide range of applications in education. In this paper, we review Latent Dirichlet...
Text mining has a wide range of applications in education. In this paper, we review Latent Dirichlet...
Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal wit...
Probabilistic topic models, such as LDA, are standard text analysis algorithms that provide predicti...
In this paper, we present preliminary work on identifying equivalent structures required by LDA, and...