• Fair exposure ≠ fair outcomes. Visibility in rankings does not guarantee equitable shortlisting in online recruitment. • Human and task factors matter. Recruiter behavior, task design, and candidate cues influence fairness beyond algorithms, as we show in user studies. • Design implications. Our findings translate into practical implications for model evaluations, interfaces, and recruiter practices to support equity. Personnel recruitment is increasingly mediated by Applicant Tracking Systems (ATS), which rank candidates for job positions, making them a central decision-support tool in modern Human Resources (HR) processes. Often framed as an information retrieval (IR) problem, the ranking of candidates in ATS is typically driven by relevance to the job position, with algorithms sorting applicants according to a set of predefined criteria. In recent years, fairness-aware ranking methods have emerged to mitigate the risk of indirect discrimination, where the ordering of candidates may inadvertently favor one demographic group over another. These approaches are inspired by browsing models developed for web search and aim to balance candidate exposure based on protected characteristics. However, ATS in recruitment introduce unique challenges due to their high-stakes nature and the decision-making context in which they operate. In this paper, we present a series of user studies that explore the disconnect between fair exposure and fair outcomes in candidate shortlisting. We focus on how factors such as task design (e.g., how recruiters interact with candidate lists), individual representations of candidates (e.g., national origin cues), and ranking order influence both position bias and demographic balance. Our findings show that while demographic balance may be achieved in terms of ranking visibility, this does not necessarily translate to fair outcomes in terms of who gets shortlisted. Through a crowdsourced experiment and in-depth interviews with recruiters, we identify key task-level, individual, and ranking factors that mediate these effects. We conclude that fairness in ATS rankings is contingent not only on algorithmic design but also on the shortlisting tasks they support, as well as the interfaces, strategies, and assumptions that recruiters use when interacting with candidate lists. Based on these insights, we provide implications for the design of algorithms, interfaces, and recruitment processes that support fairer and more equitable recruitment outcomes.

Although widely used, the majority of current music recommender systems still focus on recommendations' accuracy, user preferences and isolated item characteristics, without evaluating other important factors, like the joint item selections and the recommendation moment. However, when it comes to playlist recommendations, additional dimensions, as well as the notion of user experience and perception, should be taken into account to improve recommendations' quality. In this work, HybA, a hybrid recommender system for automatic playlist continuation, that combines Latent Dirichlet Allocation and Case-Based Reasoning, is proposed. This system aims to address “similar concepts” rather than similar users. More than generating a playlist based on user requirements, like automatic playlist generation methods, HybA identifies the semantic characteristics of a started playlist and reuses the most similar past ones, to recommend relevant playlist continuations. In addition, support to beyond accuracy dimensions, like increased coherence or diverse items' discovery, is provided. To overcome the semantic gap between music descriptions and user preferences, identify playlist structures and capture songs' similarity, a graph model is used. Experiments on real datasets have shown that the proposed algorithm is able to outperform other state of the art techniques, in terms of accuracy, while balancing between diversity and coherence.

Recommender Systems have recently become a fundamental part of various applications intending to support the users when searching for information, products and services that could be interested in at a given moment. While the majority of the current Recommender Systems generate Isolate item recommendations based on user-item interactions, mainly expressed through ratings, this paper presents a system generating recommendations of sets of items. The objective of this system is to generate accurate recommendations in domains where users are looking for a more complete experience, in terms of joint item selections, and where the complexity of the related information cannot be evaluated and treated efficiently using the actual systems, like in the music playlist recommendations. In order to identify the underlying relations between songs, their styles and the artists that perform those, a hybrid Case-Based Reasoning approach combined with a graph model is used. This framework was designed with the scope to overcome the semantic gap present in multimedia recommendations, and at the same time, to be able to perform better in cold start situations. The experimentation results presented, show that the proposed approach is able to deliver recommendations of equal and higher accuracy than some of the widely used recommendation methods.

Cloud computing has changed considerably the architecture of modern IT systems introducing new design principles based on the notion of the service as main building unit. Decoupling the service offering from the underlying infrastructure, cloud computing allows for more efficient management of the required resources which in business level is translated into higher flexibility and increased profit. To this end, Clouds are not only an innovative technological environment but also a beneficial market for all the involved stakeholders. This market expands continuously and new service aggregations, customizations and business models are offered in order to cover the needs of all end-users. However, even in this new business ecosystem, the question still remains: “Which offering should I buy”? In this paper we present an approach for incorporating business intelligence in cloud marketplaces through a mechanism for answering this question based on the individual requirements and use profile of each particular cloud customer.

Nowadays cloud computing can be considered as a key element of modern ICT systems, changing the technological and architectural aspects that these systems are designed and managed. The number and variety of applications, exploiting the advantages of this new computing paradigm, is increasing, emerging a new market of services and resources. Modern applications, from enterprise software to mobile and social networking apps are adapted and become available through the Cloud, allowing wider adoption and advanced functionality. Besides the numerous technical and technological advancements, cloud computing also leverages new business models and value networks. To this direction more entities are involved in the service delivery process and marketplaces are created to ease the development of applications through reusability and aggregation of services and resources. In this paper we present an innovative mechanism for the resolution of the customers' requirements which enhances the process of selecting cloud services from the business point of view.