Evolving context-aware recommender systems with users in mind[Formula presented]

A context-aware recommender system (CARS) utilizes users’ context to provide personalized services. Contextual information can be derived from sensors in order to improve the accuracy of the recommendations. In this work, we focus on CARSs with high-dimensional contextual information that typically impacts the recommendation model, for example, by increasing the model's dimensionality and sparsity. Generating accurate recommendations is not enough to constitute a useful system from the user's perspective, since the use of some contextual information may cause problems, such as draining the user's battery, raising privacy concerns, and more. Previous studies suggested reducing the amount of contextual information utilized by using domain knowledge to select the most suitable information. This approach is only applicable when the set of contexts is small enough to handle and sufficient for preventing sparsity. Moreover, hand-crafted context information may not represent an optimal set of features for the recommendation process. Another approach is to compress the contextual information into a denser latent space, but this may limit the ability to explain the recommended items to the users or compromise their trust. In this paper, we present a multi-step approach for selecting low-dimensional subsets of contextual information and incorporating them explicitly within CARSs. At the core of our approach is a novel feature selection algorithm based on genetic algorithms, which outperforms state-of-the-art dimensionality reduction CARS algorithms by improving recommendation accuracy and interpretability. Over the course of evolution, thousands of diverse feature subsets are generated; a deep context-aware model is produced for each feature subset, and the subsets are stacked together. The resulting stacked model is accurate and only uses interpretable, explicit features. Our approach includes a mechanism of tuning the different underlying algorithms that affect user concerns, such as privacy and battery consumption. We evaluated our approach on two high-dimensional context-aware datasets derived from smartphones. An empirical analysis of our results confirms that our proposed approach outperforms state-of-the-art CARS models while improving transparency and interpretability for the user. In addition to the empirical results, we present several use cases, examples and methodology of how researchers, domain experts and CARS modelers can tweak the feature selection algorithm to improve various user concerns and interpretability.