The goal of provider fairness in recommender systems is to ensure equity by suggesting products from diverse providers or provider groups. When group fairness is among the goals of a system, coarse groups are frequently used, since there are typically few provider groups (e.g., two genders, or three/four age groups) and the number of items per group is large. Practically speaking, having fewer groups makes it easier for a platform to oversee how equity is distributed among them. Nevertheless, there are sensitive attributes, such as the age or the geographic provenance of the providers, that can be characterized at a fine granularity (e.g., one might group providers at the country level, instead of the continent one), which increases the number of groups and decreases the number of items per group. This study reveals that state-of-the-art models often fail to adequately recommend fine-grained provider groups when only coarse-grained groups are considered. This oversight can result in a fairness approach that, while adequate for broader demographic groups, neglects the needs of smaller subgroups. To address this disparity, we introduce CONFIGRE (COarse aNd FIne GRained Equity), an approach designed to balance equity across both coarse and fine-grained provider groups. Our methodology ensures that fairness is not only maintained at a broad demographic level but is also extended to more precisely defined groups, offering a more nuanced and comprehensive equity management in recommender systems. • We show the limitations of using coarse-grained groups in recommender systems for ensuring provider fairness. • We present CONFIGRE, a novel approach to balance equity across both coarse and fine-grained provider groups. • We validate, via experiments on real-world data, that CONFIGRE extends fairness beyond broad demographic levels to more precisely defined provider groups.

Nowadays a recommendation model should exploit additional information from both the user and item perspectives, in addition to utilizing user-item interaction data. Datasets are central in offering the required information for evaluating new models or algorithms. Although there are many datasets in the literature with user and item properties, there are several issues not covered yet: (i) it is difficult to perform cross-analysis of properties at user and item level as they are not related in most cases; and (ii) on top of that, in many occasions datasets do not allow analysis at different granularity levels. In this paper, we propose a new dataset in the music domain, named AMBAR, that tackles the above-mentioned issues. Besides detailing in depth the structure of the new dataset, we also show its application in contexts (i.e., multi-objective, fair, and calibrated recommendations) where both the effectiveness and the beyond-accuracy perspectives of recommendation are assessed.

Provider fairness aims at regulating the recommendation lists, so that the items of different providers/provider groups are suggested by respecting notions of equity. When group fairness is among the goals of a system, a common way is to use coarse groups since the number of considered provider groups is usually small (e.g., two genders, or three/four age groups) and the number of items per group is large. From a practical point of view, having few groups makes it easier for a platform to manage the distribution of equity among them. Nevertheless, there are sensitive attributes, such as the age or the geographic provenance of the providers that can be characterized at a fine granularity (e.g., one might group providers at the country level, instead of the continent one), which increases the number of groups and decrements the number of items per group. In this study, we show that, in large demographic groups, when considering coarse-grained provider groups, the fine-grained provider groups are under-recommended by the state-of-the-art models. To overcome this issue, in this paper, we present an approach that brings equity to both coarse and fine-grained provider groups. Experiments on two real-world datasets show the effectiveness of our approach.

Recent observational studies highlight the importance of considering the interactions between users in the group recommendation process, but to date their integration has been marginal. In this article, we propose a collaborative model based on the social interactions that take place in a web-based conversational group recommender system. The collaborative model allows the group recommender to implicitly infer the different roles within the group, namely, collaborative and leader user(s). Moreover, it serves as the basis of several novel collaboration-based consensus strategies that integrate both individual and social interactions in the group recommendation process. A live-user evaluation confirms that our approach accurately identifies the collaborative and leader users in a group and produces more effective recommendations.

This paper presents the results of the HDSRC 2014 competition on handwritten digit string recognition in challenging datasets organized in conjunction with ICFHR 2014. The general objective of this competition is to identify, evaluate and compare recent developments in Western Arabic digit string recognition with varying length. In addition, this competition introduces two new challenging datasets for benchmarking. We describe competition details including the datasets and evaluation measures used, and give a comparative performance analysis of six (6) participating methods along with a short description of the respective methodologies.