Pairwise interaction tensor factorization for personalized tag recommendation

Rendle, Steffen; Schmidt-Thieme, Lars

doi:10.1145/1718487.1718498

Cited by 567 publications

(397 citation statements)

References 19 publications

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“…In tensor factorization, the Canonical Decomposition (CD) [49] model simplifies the approximation of tensor C as a sum of 3-fold outer-products with rank-K decompo-…”

Section: Factorizing Indexing Resultsmentioning

confidence: 99%

Computer Vision for Lifelogging

Wang

Sun

Smeaton

et al. 2018

Computer Vision for Assistive Healthcare

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“…In tensor factorization, the Canonical Decomposition (CD) [49] model simplifies the approximation of tensor C as a sum of 3-fold outer-products with rank-K decompo-…”

Section: Factorizing Indexing Resultsmentioning

confidence: 99%

Computer Vision for Lifelogging

Wang

Sun

Smeaton

et al. 2018

Computer Vision for Assistive Healthcare

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“…Tensorial representations were already used, for instance, in (Basilico and Hofmann, 2004;Rendle and Schmidt-Thieme, 2010;Pahikkala et al, 2012). We report a comparison between factorization methods and this SVM approach in the experimental section using a real dataset.…”

Section: Related Workmentioning

confidence: 99%

Mapping preferences into Euclidean space

Luaces

Díez

Joachims

et al. 2015

Expert Systems with Applications

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Understanding and modeling human preferences is one of the key problems in applications ranging from marketing to automated recommendation. In this paper, we focus on learning and analyzing the preferences of consumers regarding food products. In particular, we explore machine learning methods that embed consumers and products in an Euclidean space such that their relationship to each other models consumer preferences. In addition to predicting preferences that were not explicitly stated, the Euclidean embedding enables visualization and clustering to understand the overall structure of a population of consumers and their preferences regarding the set of products.Notice that consumers' clusters are market segments, and products clusters can be seen as groups of similar items with respect to consumer tastes. We explore two types of Euclidean embedding of preferences, one based on inner products and one based on distances. Using a real world dataset about consumers of beef meat, we find that both embeddings produce more accurate models than a tensorial approach that uses a SVM to learn preferences. The * Corresponding author: Tel: +34 985 182 588Email addresses: oluaces@uniovi.es (Oscar Luaces), jdiez@uniovi.es (Jorge Díez), tj@cs.cornell.edu (Thorsten Joachims), abahamonde@uniovi.es (Antonio Bahamonde) Preprint submitted to Expert Systems with ApplicationsJuly 15, 2015 reason is that the number of parameters to learned in embeddings can be considerably lower than in the tensorial approach. Furthermore, we demonstrate that the visualization of the learned embeddings provides interesting insights into the structure of the consumer and product space, and that it provides a method for qualitatively explaining consumer preferences. Additionally, it is important to emphasize that the approach presented here is flexible enough to allow its use with different levels of knowledge about consumers or products; therefore the application field is very wide to grasp an accurate understanding of consumers' preferences.

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“…The model presented in Section 3.2 is a bit different since the closeness variation is not included in libFM. But the general idea, presented also in previous papers [17,18] is also based on the logistic sigmoid and maximum likelihood estimation solved using Stochastic Gradient Descend (SGD). Other optimization methods can be used in libFM, but SGD is the most recommendable according to the authors.…”

Section: Related Workmentioning

confidence: 99%

Analysis of nutrition data by means of a matrix factorization method

et al. 2015

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We present a factorization framework to analyze the data of a regression learning task with two peculiarities. First, inputs can be split into two parts that represent semantically significant entities. Second, the performance of regressors is very low. The basic idea of the approach presented here is to try to learn the ordering relations of the target variable instead of its exact value. Each part of the input is mapped into a common Euclidean space in such a way that the distance in the common space is the representation of the interaction of both parts of the input. The factorization approach obtains reliable models from which it is possible to compute a ranking of the features according to their responsibility in the variation of the target variable. Additionally, the Euclidean representation of data provides a visualization where metric properties have a clear semantics. We illustrate the approach with a case study: the analysis of a dataset about the variations of Body Mass Index for Age of children after a Food Aid Program deployed in poor rural communities in Southern México. In this case the two parts of inputs are the vectorial representation of children and their diets. In addition to discover latent information, the mapping of inputs allows us to visualize children and diets in a common metric space.

show abstract

Pairwise interaction tensor factorization for personalized tag recommendation

Cited by 567 publications

References 19 publications

Computer Vision for Lifelogging

Computer Vision for Lifelogging

Mapping preferences into Euclidean space

Analysis of nutrition data by means of a matrix factorization method

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