Name: Antonela
Type: User
Company: ISISTAN - CONICET
Bio: I am currently a Researcher at CONICET and an Adjunct Professor at UNICEN. My main research interests include social computing and recommender systems.
Twitter: tommantonela89
Location: Tandil, Buenos Aires, Argentina
Blog: tommantonela.github.io
Antonela's Projects
Repository for "The JavaScript Package Selection Task: A Comparative Experiment Using an LLM-based Approach"
Tool for architectural smell prediction
Reproducibility kit for the paper "A Sensitivity Analysis for an Architectural DebtIndex based on Architectural Smells" submitted to ECSA 2019
Companion repository for the paper "I Want to Break Free! Recommending Friends from Outside the Echo Chamber" accepted at RecSys 2021
A Folium and Streamlit Golden Retriever sightings map.
graphoW is a Python package for the creation of a Graph-of-Words (GoW) representation of texts.
Repository corresponding to the ICSA paper "Can Network Analysis Techniques help to Predict Design Dependencies? An Initial Study". Available at https://arxiv.org/abs/1808.02776
En este repositorio hay notebooks que fui preparando como soporte de Python para diversos cursos en la Facultad de Ciencias Exactas, UNICEN.
Repository for "Recommendation fairness and where to find it: An empirical study on fairness of user recommender systems"
Companion repository of the paper "Do recommender systems make social media more susceptible to misinformation spreaders?" accepted at RecSys 2022.
Repository corresponding to the SCAM paper "Towards Anticipation of Architectural Smells using Link Prediction Techniques". Available at https://arxiv.org/abs/1808.06362
Tool for Prioritizing Architecture-Sensitive Smells based on a Technical Debt Index
SMArtOp -- Sparse Matrix library for ARiThmetic Operations
My personal repository
Companion repository for the paper "Following the trail of fake news spreaders in social media: A deep learning model", published in UMAP.
Companion repository for the paper "Havenβt I just listened to this?: Exploring diversity in music recommendations", published in UMAP 2022
Urban data datasets