A latent model for collaborative filtering

Publication: Research - peer-reviewJournal article

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A latent model for collaborative filtering. / Langseth, Helge; Nielsen, Thomas Dyhre.

In: International Journal of Approximate Reasoning, Vol. 53, No. 4, 06.2012, p. 447–466.

Publication: Research - peer-reviewJournal article

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Langseth, H., & Nielsen, T. D. (2012). A latent model for collaborative filtering. International Journal of Approximate Reasoning, 53(4), 447–466doi: 10.1016/j.ijar.2011.11.002

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Author

Langseth, Helge; Nielsen, Thomas Dyhre / A latent model for collaborative filtering.

In: International Journal of Approximate Reasoning, Vol. 53, No. 4, 06.2012, p. 447–466.

Publication: Research - peer-reviewJournal article

Bibtex

@article{557f06d848c946f58fa35366ebd85d2a,
title = "A latent model for collaborative filtering",
publisher = "Elsevier Inc.",
author = "Helge Langseth and Nielsen, {Thomas Dyhre}",
year = "2012",
volume = "53",
number = "4",
pages = "447–466",
journal = "International Journal of Approximate Reasoning",
issn = "0888-613X",

}

RIS

TY - JOUR

T1 - A latent model for collaborative filtering

A1 - Langseth,Helge

A1 - Nielsen,Thomas Dyhre

AU - Langseth,Helge

AU - Nielsen,Thomas Dyhre

PB - Elsevier Inc.

PY - 2012/6

Y1 - 2012/6

N2 - Recommender systems based on collaborative filtering have received a great deal of interest over the last two decades. In particular, recently proposed methods based on dimensionality reduction techniques and using a symmetrical representation of users and items have shown promising results. Following this line of research, we propose a probabilistic collaborative filtering model that explicitly represents all items and users simultaneously in the model. Experimental results show that the proposed system obtains significantly better results than other collaborative filtering systems (evaluated on the MovieLens data set). Furthermore, the explicit representation of all users and items allows the model to, e.g., make group-based recommendations balancing the preferences of the individual users.

AB - Recommender systems based on collaborative filtering have received a great deal of interest over the last two decades. In particular, recently proposed methods based on dimensionality reduction techniques and using a symmetrical representation of users and items have shown promising results. Following this line of research, we propose a probabilistic collaborative filtering model that explicitly represents all items and users simultaneously in the model. Experimental results show that the proposed system obtains significantly better results than other collaborative filtering systems (evaluated on the MovieLens data set). Furthermore, the explicit representation of all users and items allows the model to, e.g., make group-based recommendations balancing the preferences of the individual users.

U2 - 10.1016/j.ijar.2011.11.002

DO - 10.1016/j.ijar.2011.11.002

JO - International Journal of Approximate Reasoning

JF - International Journal of Approximate Reasoning

SN - 0888-613X

IS - 4

VL - 53

SP - 447

EP - 466

ER -