Name
Abstract | ||
|---|---|---|
Recommender systems (RSs) have attained exceptional performance in learning users\u0027 preferences and finding the most suitable products. Recent advances in adversarial machine learning (AML) in computer vision have raised interests in recommenders\u0027 security.It has been demonstrated that widely adopted model-based recommenders, e.g., BPR-MF, are not robust to adversarial perturbations added on the learned parameters, e.g., users\u0027 embeddings, which can cause drastic reduction of recommendation accuracy.However, the state-of-the-art adversarial method, named fast gradient sign method (FGSM), builds the perturbation with a single-step procedure. In this work, we extend the FGSM method proposing multi-step adversarial perturbation (MSAP) procedures to study the recommenders\u0027 robustness under powerful methods. Letting fixed the perturbation magnitude, we illustrate that MSAP is much more harmful than FGSM in corrupting the recommendation performance of BPR-MF. Then, we assess the MSAP efficacy on a robustified version of BPR-MF, i.e., AMF. Finally, we analyze the variations of fairness measurements on each perturbed recommender. Code and data are available at https://github.com/sisinflab/MSAP. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.32473/flairs.v34i1.128443 | FLAIRS Conference |
DocType | Volume | Issue |
Conference | 34 | 1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vito Walter Anelli | 1 | 91 | 18.45 |
| Alejandro Bellogín | 2 | 0 | 0.34 |
| Yashar Deldjoo | 3 | 186 | 24.74 |
| Tommaso Di Noia | 4 | 0 | 0.34 |
| Felice Antonio Merra | 5 | 32 | 6.46 |