Spatial Processes for Recommender Systems
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 2022-2026.
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
@inproceedings{ijcai2009p333,
title = {Spatial Processes for Recommender Systems},
author = {Bohnert, Fabian and Schmidt, Daniel Francis and Zukerman, Ingrid},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {2022--2026},
year = {2009},
url = {https://ijcai.org/proceedings/2009/333},
}
TY - CPAPER TI - Spatial Processes for Recommender Systems AU - Bohnert, Fabian AU - Schmidt, Daniel Francis AU - Zukerman, Ingrid T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 2022 EP - 2026 UR - https://ijcai.org/proceedings/2009/333 ER -Copied
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