---
title: "Spatial Processes for Recommender Systems"
authors:
  - "Fabian Bohnert"
  - "Daniel Francis Schmidt"
  - "Ingrid Zukerman"
doi: ""
url: "https://ijcai.org/proceedings/2009/333"
pdf: "https://ijcai.org/proceedings/2009/0333.pdf"
venue: "Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence"
year: 2009
pages: "2022-2026"
track: ""
keywords:
---

# Spatial Processes for Recommender Systems

Fabian Bohnert, Daniel Francis Schmidt, Ingrid Zukerman

*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