Modeling User Rating Profiles for Collaborative Filtering
Abstract
In this paper we present a generative latent variable model for rating-based collaborative (cid:12)ltering called the User Rating Pro(cid:12)le model (URP). The generative process which underlies URP is de- signed to produce complete user rating pro(cid:12)les, an assignment of one rating to each item for each user. Our model represents each user as a mixture of user attitudes, and the mixing proportions are distributed according to a Dirichlet random variable. The rating for each item is generated by selecting a user attitude for the item, and then selecting a rating according to the preference pattern associ- ated with that attitude. URP is related to several models including a multinomial mixture model, the aspect model [7], and LDA [1], but has clear advantages over each.
Cite
Text
Marlin. "Modeling User Rating Profiles for Collaborative Filtering." Neural Information Processing Systems, 2003.Markdown
[Marlin. "Modeling User Rating Profiles for Collaborative Filtering." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/marlin2003neurips-modeling/)BibTeX
@inproceedings{marlin2003neurips-modeling,
title = {{Modeling User Rating Profiles for Collaborative Filtering}},
author = {Marlin, Benjamin M.},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {627-634},
url = {https://mlanthology.org/neurips/2003/marlin2003neurips-modeling/}
}