Spatial Processes for Recommender Systems
Abstract
Spatial processes are typically used to analyse and predict geographic data. This paper adapts such models to predicting a user's interests (i.e., implicit item ratings) within a recommender system in the museum domain. We present the theoretical framework for a model based on Gaussian spatial processes, and discuss efficient algorithms for parameter estimation. Our model was evaluated with a real-world dataset collected by tracking visitors in a museum, attaining a higher predictive accuracy than state-of-the-art collaborative filters. Fabian Bohnert, Daniel F. Schmidt, Ingrid Zukerman
Cite
Text
Bohnert et al. "Spatial Processes for Recommender Systems." International Joint Conference on Artificial Intelligence, 2009.Markdown
[Bohnert et al. "Spatial Processes for Recommender Systems." International Joint Conference on Artificial Intelligence, 2009.](https://mlanthology.org/ijcai/2009/bohnert2009ijcai-spatial/)BibTeX
@inproceedings{bohnert2009ijcai-spatial,
title = {{Spatial Processes for Recommender Systems}},
author = {Bohnert, Fabian and Schmidt, Daniel Francis and Zukerman, Ingrid},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2009},
pages = {2022-2027},
url = {https://mlanthology.org/ijcai/2009/bohnert2009ijcai-spatial/}
}