Pinned Repositories
adversarial-recommender-systems-survey
The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. In this survey, we provide an exhaustive literature review of 74 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community, working on the security of RS or on generative models using GANs to improve their quality.
amlrecsys-tutorial
Tutorial by Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia and Felice Antonio Merra about Adversarial Machine Learning in Recommender Systems
Ducho
Ducho is a Python framework aimed to extract multimodal features used in multimodal recommendation settings through a highly-customizable processing and extraction pipeline.
elliot
Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
FedBPR
Official implementation of the papers "User-controlled federated matrix factorization for recommender systems" and "FedeRank: User Controlled Feedback with Federated Recommender Systems"
KGFlex
Official implementation of the paper "Sparse Feature Factorization for Recommender Systems with Knowledge Graphs"
lodreclib
lodreclib is a Java library to build recommendation engines which exploit the information encoded in Linked (Open) Data datasets.
LODrecsys-datasets
Here, we provide mappings to DBpedia resources of items in well known datasets to evaluate recommender systems. This can allows practitioners in the field to evaluate and compare their algorithms with existing approaches.
recsys2021-pursuing-privacy
Material supporting the tutorial "Pursuing Privacy in Recommender Systems: The View of Users and Researchers from Regulations to Applications" held at the 15th ACM Conference on Recommender Systems in Amsterdam, Netherlands
Reenvisioning-the-comparison-between-Neural-Collaborative-Filtering-and-Matrix-Factorization
Information Systems Lab @ Polytechnic University of Bari's Repositories
sisinflab/elliot
Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
sisinflab/Ducho
Ducho is a Python framework aimed to extract multimodal features used in multimodal recommendation settings through a highly-customizable processing and extraction pipeline.
sisinflab/Agent-Based-Artificial-Intelligence
Codes for hands-on lessons
sisinflab/Formal-MultiMod-Rec
Formalizing Multimedia Recommendation through Multimodal Deep Learning, accepted in ACM Transactions on Recommender Systems.
sisinflab/Recommender-ChatGPT
The official source code and datasets for the paper titled "Evaluating ChatGPT as a Recommender System: A Rigorous Approach"
sisinflab/KGTORe
Official implementation of the paper "KG-TORE: Tailored recommendations through knowledge-aware GNN models" accepted at RecSys 2023
sisinflab/personalized-popularity-awareness
sisinflab/Ducho-meets-Elliot
sisinflab/Multimodal-Feature-Extractor
A Python implementation to extract multimodal features (visual and textual).
sisinflab/DataRec
sisinflab/Graph-Missing-Modalities
Accepted as a short paper at CIKM 2024.
sisinflab/KGUF
sisinflab/Agent-Based-Artificial-Intelligence-24-25
sisinflab/DIVAN
sisinflab/Edge-Graph-Collaborative-Filtering
Accepted as full paper at DL4SR@CIKM2022
sisinflab/fondamenti-web-2023-2024
Soluzioni complete per le esercitazioni di Fondamenti del Web
sisinflab/Enhancing-Reproducibility-in-Recommender-Systems
Official GitHub repository of the lecture "Enhancing Reproducibility in Recommender Systems: A Path Towards Scientific Integrity and Effective Implementation", at the 2024 ACM RecSys Summer School
sisinflab/Guidelines-Topology-GNNs4RecSys
sisinflab/LHider
sisinflab/Multimodal-RSs-Reproducibility
sisinflab/NeuroSense
A Novel EEG Dataset Utilizing Low-Cost, Sparse Electrode Devices for Emotion Exploration
sisinflab/Topology-Graph-Collaborative-Filtering
Accepted as a long paper at RecSys 2024 in the Reproducibility Track.
sisinflab/DiCE
Generate Diverse Counterfactual Explanations for any machine learning model.
sisinflab/EngineeredIntelligentSystems
sisinflab/GeCo
sisinflab/ijhcs-user-in-rs
sisinflab/PRONTO
sisinflab/tutorial-gnns-recsys-log2023
Website for the tutorial "Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node Representation" accepted at LoG 2023
sisinflab/webium24
sisinflab/webium25
2nd Workshop on Wearable Devices and Brain-Computer Interfaces for User Modelling, WeBIUM