Detection of Trending Topic Communities: Bridging Content Creators and Distributors

The rise of a trending topic on Twitter or Facebook leads to the temporal emergence of a set of users currently interested in that topic. Given the temporary nature of the links between these users, being able to dynamically identify communities of users related to this trending topic would allow for a rapid spread of information. Indeed, individual users inside a community might receive recommendations of content generated by the other users, or the community as a whole could receive group recommendations, with new content related to that trending topic. In this paper, we tackle this challenge, by identifying coherent topic-dependent user groups, linking those who generate the content (creators) and those who spread this content, e.g., by retweeting/reposting it (distributors). This is a novel problem on group-to-group interactions in the context of recommender systems. Analysis on real-world Twitter data compare our proposal with a baseline approach that considers the retweeting activity, and validate it with standard metrics. Results show the effectiveness of our approach to identify communities interested in a topic where each includes content creators and content distributors, facilitating users' interactions and the spread of new information.Comment: 9 pages, 4 figures, 2 tables, Hypertext 2017 conferenc

Reingeniería del circuito administrativo. El caso de la reestructuración organizacional en una unidad académica de una universidad pública.

El análisis de una organización así como la planificación y ejecución de sus proyectos se basa en decisiones tomadas sobre datos e información, indispensables para el control y mejora de la gestión desde una visión integral. Las observaciones realizadas durante el proceso de Autoevaluación 2004 de la Facultad de Ciencias Agrarias de la Universidad Nacional de Lomas de Zamora condujeron a la necesidad de contar con un sistema informático que simplificara e hiciera más eficiente la tarea del área administrativa a la vez que congruente con el sistema solicitado por la Secretaría de Políticas Universitarias, de manera de poder funcionar de forma funcional comunicacionalmente articulada. El presente trabajo describe la reestructuración llevada a cabo en el área administrativa en forma de reconcepción y rediseño de los procesos (Hammer y Champy, 1994), para poder comenzar con la implementación de un nuevo sistema de información que integre la organización toda. El desafío constituía en lograr una solución integral que contemplara la heterogeneidad de situaciones y de necesidades existentes (Gurmendi y Williams, 2006)

Evaluating the Prediction Bias Induced by Label Imbalance in Multi-label Classification

Prediction bias is a well-known problem in classification algorithms, which tend to be skewed towards more represented classes. This phenomenon is even more remarkable in multi-label scenarios, where the number of underrepresented classes is usually larger. In light of this, we hereby present the Prediction Bias Coefficient (PBC), a novel measure that aims to assess the bias induced by label imbalance in multi-label classification. The approach leverages Spearman's rank correlation coefficient between the label frequencies and the F-scores obtained for each label individually. After describing the theoretical properties of the proposed indicator, we illustrate its behaviour on a classification task performed with state-of-the-art methods on two real-world datasets, and we compare it experimentally with other metrics described in the literature

Looks Can Be Deceiving: Linking User-Item Interactions and User's Propensity Towards Multi-Objective Recommendations

Multi-objective recommender systems (MORS) provide suggestions to users according to multiple (and possibly conflicting) goals. When a system optimizes its results at the individual-user level, it tailors them on a user's propensity towards the different objectives. Hence, the capability to understand users' fine-grained needs towards each goal is crucial. In this paper, we present the results of a user study in which we monitored the way users interacted with recommended items, as well as their self-proclaimed propensities towards relevance, novelty and diversity objectives. The study was divided into several sessions, where users evaluated recommendation lists originating from a relevance-only single-objective baseline as well as MORS. We show that despite MORS-based recommendations attracted less selections, its presence in the early sessions is crucial for users' satisfaction in the later stages. Surprisingly, the self-proclaimed willingness of users to interact with novel and diverse items is not always reflected in the recommendations they accept. Post-study questionnaires provide insights on how to deal with this matter, suggesting that MORS-based results should be accompanied by elements that allow users to understand the recommendations, so as to facilitate their acceptance.Comment: Accepted as a short paper at ACM RecSys 2023 conference. See https://doi.org/10.1145/3604915.360884

Interplay between upsampling and regularization for provider fairness in recommender systems

Considering the impact of recommendations on item providers is one of the duties of multi-sided recommender systems. Item providers are key stakeholders in online platforms, and their earnings and plans are influenced by the exposure their items receive in recommended lists. Prior work showed that certain minority groups of providers, characterized by a common sensitive attribute (e.g., gender or race), are being disproportionately affected by indirect and unintentional discrimination. Our study in this paper handles a situation where (i) the same provider is associated with multiple items of a list suggested to a user, (ii) an item is created by more than one provider jointly, and (iii) predicted user–item relevance scores are biasedly estimated for items of provider groups. Under this scenario, we assess disparities in relevance, visibility, and exposure, by simulating diverse representations of the minority group in the catalog and the interactions. Based on emerged unfair outcomes, we devise a treatment that combines observation upsampling and loss regularization, while learning user–item relevance scores. Experiments on real-world data demonstrate that our treatment leads to lower disparate relevance. The resulting recommended lists show fairer visibility and exposure, higher minority item coverage, and negligible loss in recommendation utility

Robust reputation independence in ranking systems for multiple sensitive attributes

Ranking systems have an unprecedented influence on how and what information people access, and their impact on our society is being analyzed from different perspectives, such as users’ discrimination. A notable example is represented by reputation-based ranking systems, a class of systems that rely on users’ reputation to generate a non-personalized item-ranking, proved to be biased against certain demographic classes. To safeguard that a given sensitive user’s attribute does not systematically affect the reputation of that user, prior work has operationalized a reputation independence constraint on this class of systems. In this paper, we uncover that guaranteeing reputation independence for a single sensitive attribute is not enough. When mitigating biases based on one sensitive attribute (e.g., gender), the final ranking might still be biased against certain demographic groups formed based on another attribute (e.g., age). Hence, we propose a novel approach to introduce reputation independence for multiple sensitive attributes simultaneously. We then analyze the extent to which our approach impacts on discrimination and other important properties of the ranking system, such as its quality and robustness against attacks. Experiments on two real-world datasets show that our approach leads to less biased rankings with respect to multiple users’ sensitive attributes, without affecting the system’s quality and robustness

Combining mitigation treatments against biases in personalized rankings: Use case on item popularity

Historical interactions leveraged by recommender systems are often non-uniformly distributed across items. Though they are of interest for consumers, certain items end up therefore being biasedly under-recommended. Existing treatments for mitigating these biases act at a single step of the pipeline (either pre-, in-, or post-processing), and it remains unanswered whether simultaneously introducing treatments throughout the pipeline leads to a better mitigation. In this paper, we analyze the impact of bias treatments along the steps of the pipeline under a use case on popularity bias. Experiments show that, with small losses in accuracy, the combination of treatments leads to better trade-offs than treatments applied separately. Our findings call for treatments rooting out bias at different steps simultaneously

Espacialização e geração de mapas temáticos das condições de cobertura dos solos para a região Norte de MG, a partir de análises espectrais de imagens do satélite Landsat-5 TM.

Este trabalho teve como objetivo realizar uma análise do comportamento espectral das classes de cobertura vegetal do Norte de Minas Gerais, na região semi-árida do médio São Francisco, onde estão inseridos os municípios de Nova Porteirinha (Sítio de Fenotipagem de seleção de genótipos de cereais para a Tolerância à Seca - Embrapa Milho e Sorgo) e Janaúba e os Perímetros Irrigados de Gorutuba e Jaíba, por meio de técnicas de sensoriamento remoto (imagens de um satélite) e de geoprocessamento (SIG), visando espacializar as condições de cobertura dos solos, utilizando como metodologia o cálculo do NDVI (?Normalized Difference Vegetation Index?) e Kc (coeficiente de cultura) para gerar mapas temáticos das condições da cobertura do solo e do uso da água. O NDVI calculado com base na imagem Landsat-5 TM foi eficiente na detecção de áreas com diferentes coberturas vegetais e a equação linear utilizada na espacialização do Kc apresentou resultados satisfatórios