@inproceedings{Hofmann:2003,
title = {Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis},
author = {Thomas Hofmann},
crossref = {Callan:2003},
url = {http://www.cs.brown.edu/~th/papers/Hofmann-SIGIR2003.pdf},
year = {2003},
description = {imported},
abstract = {Collaborative Filtering aims at learning predictive models of user preferences, interests or behavior from community data, i.e. a database of available user preferences. In this paper, we describe a new model-based algorithm designed for this task, which is based on a generalization of probabilistic latent semantic analysis to continuous-valued response variables. More specically, we assume that the observed user ratings can be modeled as a mixture of user communities or interest groups, where users may participate probabilistically in one or more groups. Each community is characterized by a Gaussian distribution on the normalized ratings for each item. The normalization of ratings is performed in a user-specific manner to account for variations in absolute shift and variance of ratings. Experiments on the Each-Movie data set show that the proposed approach compares favorably with other collaborative Filtering techniques.},
keywords = {imported }
}
::