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Nowadays, we are inundated with data gathered from the users, both in the realand in the digital-world. Data streams come from sensors (e.g., environmental) and devices (e.g., activity trackers, medical devices, and cameras) connected together according to Internet of Things (IoT) protocols, social media continuously updated with text, photos, and videos, as well as geolocalizated systems that send the user’s location in real time. Thus, it is more and more required to mine those data searching for patterns and rules to guide intelligent systems to provide recommendations, feedback, suggestions, and nudge in a personalized and adaptive way. The goal is to create a snapshot, or profile, of the user by understanding her/his behavior when searching for a product, activities when near to a store, and the influence of a recommendation in a social media. Data tells us the much of the user’s story and we have to complement it through tools and techniques able to look for patterns that have to be turned into knowledge to guide intelligent algorithms and systems in making smarter recommendations. In other words, our goal is to transform data into knowledge to support and enhance the user’s experience. Being interested in making recommender systems smarter and more responsive to user’s needs, we first need to better understand the users. In that direction, one important requirement is to allow the users to provide feedback regarding the recommendations provided by the system. Moreover, we have to take into consideration the role of social media in influencing users’ decision-making. The focus of this special issue is to explore how data-driven behavioral modeling can be used to generate knowledge that can feed algorithms and systems.

Data-driven user behavioral modeling: from real-world behavior to knowledge, algorithms, and systems