Mexican International Conference on Artificial Intelligence

Advances in Artificial Intelligence and Its Applications pp 67-79 | Cite as

SVD-LDA: Topic Modeling for Full-Text Recommender Systems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9414)


In recommender systems, matrix decompositions, in particular singular value decomposition (SVD), represent users and items as vectors of features and allow for additional terms in the decomposition to account for other available information. In text mining, topic modeling, in particular latent Dirichlet allocation (LDA), are designed to extract topical content of a large corpus of documents. In this work, we present a unified SVD-LDA model that aims to improve SVD-based recommendations for items with textual content with topic modeling of this content. We develop a training algorithm for SVD-LDA based on a first order approximation to Gibbs sampling and show significant improvements in recommendation quality.



This work was supported by the Samsung Research Center grant “Recommendation Systems based on Probabilistic Graphical Models”, the Government of the Russian Federation grant 14.Z50.31.0030, and the Russian Foundation for Basic Research grant no. 15-29-01173.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Steklov Institute of Mathematics at St. PetersburgSt. PetersburgRussia
  2. 2.Laboratory for Internet Studies, National Research University – Higher School of EconomicsSt. PetersburgRussia
  3. 3.Kazan (Volga Region) Federal UniversityKazanRussia

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