Special Issue on Information Retrieval, Recommender Systems and Adaptive Systems

The current spread of the Internet across an ever-increasing number of devices, in- cluding mobile and IoT devices, has created an enormous flow of data. Therefore, in the era of big data, there is an increasing need for tools to support the end user in their exploration. It is common for the end user to spend several hours finding what they are looking for and need on the web. In this regard, adaptive and personalized systems play an increasingly important role in our daily lives, as we increasingly rely on systems that adapt their behavior according to our preferences and needs, and support us in a wide range of heterogeneous decision-making tasks. Among various information filtering and retrieval techniques, recommender systems have proven to be a valuable tool in addressing the problem of information overload. Specifically, a recommender system is capable of di- recting a user to a new not-yet-considered item that the algorithm believes may be relevant to the current needs and context of use. The user can then browse the recommendations, choose whether or not to accept them, and provide (immediately or at a later stage) implicit or explicit feedback. User actions and feedback can be stored in a database for use in generating new recommendations in future user–system interactions. This process leads to a constant increase in system performance based on the number of interactions with the system.