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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.

adverse2021

agent-based-artificial-intelligence

Codes for hands-on lessons

amlrecsys-tutorial

Tutorial by Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia and Felice Antonio Merra about Adversarial Machine Learning in Recommender Systems

anna

Vocal Assistant / Chatbot Anna to explore Puglia Digital Library

augmented-and-linked-open-datasets-for-recommendation

clientaware-fl

cnns-in-vrss

In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops

content-style-vrss

Official implementation of the paper "Leveraging Content-Style Item Representation for Visual Recommendation" accepted at ECIR 2022

cp-theories-sparql

datasetssplits

This is a collection of splittings of publicly available Datasets. This collection has been created for two main purposes:

denoise-to-protect-vrs

dlpreferences

Reasoning with preferences in Description Logics

ducho

Python framework to extract multimodal features for multimodal recommendation in a highly-customizable way.

ecir2023-graph-cf

Accepted as full paper at ECIR 2023

edge-graph-collaborative-filtering

Accepted as full paper at DL4SR@CIKM2022

elliot

Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

features-factorization

Features-Factorization and Feature Spreading Relevance (Knowledge-aware Recommender Systems)

fedbpr

Official implementation of the papers "User-controlled federated matrix factorization for recommender systems" and "FedeRank: User Controlled Feedback with Federated Recommender Systems"

fondamenti-web-2023-2024

Soluzioni complete per le esercitazioni di Fondamenti del Web

formal-multimod-rec

Formalizing Multimedia Recommendation through Multimodal Deep Learning, accepted in ACM Transactions on Recommender Systems.

fpl-results

google-hashcode-playground

This project allows to evaluate your submissions for past editions of Google HashCode.

graph-characteristics

graph-demo

Accepted as demo paper at UMAP 2023

graph-missing-modalities

graph-pareto

graph-pipeline-evaluation

graph-review

graph-rss-reproducibility

Accepted as reproducibility paper at RecSys 2023.