ACTER: Activity Customization through Timely and Explainable Recommendations

The proliferation of sensors, including wearable devices, has significantly increased the volume of generated data, opening up new opportunities for personalized recommendations. This paper presents ACTER (Activity Customization through Timely and Explainable Recommendations), an integrated framework to provide contextual, timely, explainable, and user-specific recommendations. Thanks to the sequential rule mining algorithm ALBA (AgedLookBackApriori), we extract totally ordered sequential rules to uncover hidden insights from temporal data, ultimately improving a predefined target parameter related to the selected application domain. An aging mechanism is applied to ensure that recommendations remain relevant, giving more weight to newer information while still considering older data. In addition, our framework leverages historical data to also infer personalized, contextual information, allowing us to adapt the predefined context—usually set at the design stage—more dynamically and expressly. The experimental results of the ACTER evaluation confirm that integrating ad-hoc contexts mined from historical data into the recommender system yields more accurate suggestions.