CLAYTON SCHOOL OF INFORMATION TECHNOLOGY
MONASH UNIVERSITY

TECHNICAL REPORT 2009/238

Spatial Processes for Recommender Systems

Fabian Bohnert, Daniel F. Schmidt, and Ingrid Zukerman

ABSTRACT

Spatial processes are typically used to analyse and predict geographic data. This paper adapts such models to the prediction of a user's interests or item ratings in recommender systems. 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 simulated data and a real-world dataset collected by tracking visitors in a museum, and achieves a higher predictive accuracy than a non-personalised baseline. Additionally, in the real-world scenario, the model attains a higher predictive accuracy than state-of-the-art collaborative filters.