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Spatial Processes for Recommender Systems

2019-11-15 | |  70 |   47 |   0

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 effificient 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 fifilters

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