Most modern Information Systems record user activity logs to extract knowledge and support new and higher quality services. Recommender systems (RS) in particular those based on collaborative filtering, are an example of such new services that have gained tremendous popularity. However, we know that standard activity logs are too poor to properly reflect user activity as they are influenced by many environmental factors (time, location, weather, profile, etc.), out of which a lot is not grasped in standard user activity logs. This makes it very difficult to mine users' intentions, which are key to analyze users' behavior reliably, and therefore key to quality recommendations. Several types of sources, such as sensors, external systems, outside actors, domain knowledge bases, or forecast systems could be used to obtain this information. The theory in this PhD is that the combination of multiple types of sources can extensively contribute to providing better insight into user activityt hrough context-rich intentional process mining and therefore deliver newer and higher quality recommendations. While existing research mainly focuses on log datasets; only a few contributions consider ontologies to gather multiple sources. We propose a novel approach that (a) combines different types of sources into ontologies (b) uses such ontologies for intentional process mining (c) exploits this intentional model for contextual recommendations.
Keywords: Intention Mining, Information System, Logs, Ontology, Context,