Search Results for author:
Found 13 papers, 3 papers with code
Interacting Attention-gated Recurrent Networks for Recommendation
In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions.
A User Modeling Pipeline for Studying Polarized Political Events in Social Media
In the scope of this work, our proposed pipeline is applied to two referendum scenarios (independence of Catalonia in Spain and autonomy of Lombardy in Italy) in order to assess the performance of the approach with respect to the capability of collecting correct insights on the demographics of social media users and of predicting the poll results based on the opinions shared by the users.
Social and Information Networks Computers and Society
Designing Evaluations of Machine Learning Models for Subjective Inference: The Case of Sentence Toxicity
Machine Learning (ML) is increasingly applied in real-life scenarios, raising concerns about bias in automatic decision making.
Unfairness towards subjective opinions in Machine Learning
Despite the high interest for Machine Learning (ML) in academia and industry, many issues related to the application of ML to real-life problems are yet to be addressed.
Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics
The way pages are ranked in search results influences whether the users of search engines are exposed to more homogeneous, or rather to more diverse viewpoints.
Active Learning from Crowd in Document Screening
In this paper, we explore how to efficiently combine crowdsourcing and machine intelligence for the problem of document screening, where we need to screen documents with a set of machine-learning filters.
Active Hybrid Classification
Hybrid crowd-machine classifiers can achieve superior performance by combining the cost-effectiveness of automatic classification with the accuracy of human judgment.
This Is Not What We Ordered: Exploring Why Biased Search Result Rankings Affect User Attitudes on Debated Topics
To better understand the mechanisms underlying SEME, we present a pre-registered, 5 × 3 factorial user study investigating whether order effects (i. e., users adopting the viewpoint pertaining to higher-ranked documents) can cause SEME.
Empowering Local Communities Using Artificial Intelligence
Artificial Intelligence (AI) is increasingly used to analyze large amounts of data in various practices, such as object recognition.
Towards a multi-stakeholder value-based assessment framework for algorithmic systems
We, therefore, contribute to current ML auditing practices with an assessment framework that visualizes closeness and tensions between values and we give guidelines on how to operationalize them, while opening up the evaluation and deliberation process to a wide range of stakeholders.
Metadata Representations for Queryable ML Model Zoos
Machine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the ML models and datasets that are useful for reporting, auditing, reproducibility, and interpretability purposes.
Model Selection with Model Zoo via Graph Learning
Pre-trained deep learning (DL) models are increasingly accessible in public repositories, i. e., model zoos.
Normalization of Long-tail Adverse Drug Reactions in Social Media
The automatic mapping of Adverse Drug Reaction (ADR) reports from user-generated content to concepts in a controlled medical vocabulary provides valuable insights for monitoring public health.
