A latent model for collaborative filtering
Publication: Research - peer-review › Journal 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-review › Journal article
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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 -