Discovering and Exploiting Causal Dependencies for Robust Mobile Context-Aware Recommenders

Acquisition of context poses unique challenges to mobile context-aware recommender systems. The limited resources in these systems make minimizing their context acquisition a practical need, and the uncertainty in the mobile environment makes missing and erroneous context inputs a major concern. In this paper, we propose an approach based on Bayesian networks (BNs) for building recommender systems that minimize context acquisition. Our learning approach iteratively trims the BN-based context model until it contains only the minimal set of context parameters that are important to a user. In addition, we show that a two-tiered context model can effectively capture the causal dependencies among context parameters, enabling a recommender system to compensate for missing and erroneous context inputs. We have validated our proposed techniques on a restaurant recommendation data set and a Web page recommendation data set. In both benchmark problems, the minimal sets of context can be reliably discovered for the specific users. Furthermore, the learned Bayesian network consistently outperforms the J4.8 decision tree in overcoming both missing and erroneous context inputs to generate significantly more accurate predictions.