Learning What People (Don't) Want
Abstract
Recommender systems make use of a database of user ratings to generate personalized recommendations and help people to find relevant products, items, or documents. In this paper, we present a probabilistic, model-based framework for user ratings based on a novel collaborative filtering technique that performs an automatic decomposition of user preferences. Our approach has several benefits, including highly accurate predictions, task-optimized model learning, mining of interest groups and patterns, as well as a highly efficient and scalable computation of predictions and recommendation lists.
Cite
Text
Hofmann. "Learning What People (Don't) Want." European Conference on Machine Learning, 2001. doi:10.1007/3-540-44795-4_19Markdown
[Hofmann. "Learning What People (Don't) Want." European Conference on Machine Learning, 2001.](https://mlanthology.org/ecmlpkdd/2001/hofmann2001ecml-learning/) doi:10.1007/3-540-44795-4_19BibTeX
@inproceedings{hofmann2001ecml-learning,
title = {{Learning What People (Don't) Want}},
author = {Hofmann, Thomas},
booktitle = {European Conference on Machine Learning},
year = {2001},
pages = {214-225},
doi = {10.1007/3-540-44795-4_19},
url = {https://mlanthology.org/ecmlpkdd/2001/hofmann2001ecml-learning/}
}