Recommender Systems support a broad range of domains, each with peculiarities that recommendation algorithms must consider to produce appropriate suggestions. In the paper, we bring attention to a little-studied scenario related to the news domain: recommendations catering to media journalists. Based on the particular needs inherent to a newsroom, the authors introduce SJORS, a wire news Recommender System that takes into account the activities of each journalist as well as other critical factors that arise in this particular domain, such as wire news recency. Given the nature of the items recommended, SJORS deals with the inherent ambiguity of natural language by exploiting different semantic techniques and technologies. The authors have conducted several experiments in a media company, which validated the performance and applicability of the system. Outcomes emerging from this work could be extended to other domains of interest, such as online stores, streaming platforms, or digital libraries, to name a few.@en

Reading is a fundamental skill that each person needs to develop during early childhood and continue to enhance into adulthood. While children/teenagers depend on this skill to advance academically and become educated individuals, adults are expected to acquire a certain level of proficiency in reading so that they can engage in social/civic activities and successfully participate in the workforce. A step towards assisting individuals to become lifelong readers is to provide them adequate reading selections which can cultivate their intellectual and emotional growth. With that in mind, we have developed SOLE-R, a topic map-based tool that yields book recommendations. SOLE-R takes advantage of lexical and semantic resources to infer the likes/dislikes of a reader and thus is not restricted by the syntactic constraints imposed on existing recommenders. Furthermore, SOLE-R relies on publicly-accessible data on books to perform an in-depth analysis of the preferences of a reader that goes beyond book content or reading patterns explored by existing recommenders. We have verified the correctness of SOLE-R using a popular benchmark dataset. In addition, we have compared its performance with (state-of-the-art) recommendation strategies to further demonstrate the effectiveness of SOLE-R.@en