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- short-paperJuly 2023
Denoise to Protect: A Method to Robustify Visual Recommenders from Adversaries
- Felice Antonio Merra,
- Vito Walter Anelli,
- Tommaso Di Noia,
- Daniele Malitesta,
- Alberto Carlo Maria Mancino
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 1924–1928https://doi.org/10.1145/3539618.3591971While the integration of product images enhances the recommendation performance of visual-based recommender systems (VRSs), this can make the model vulnerable to adversaries that can produce noised images capable to alter the recommendation behavior. ...
- research-articleJuly 2023
FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 3037–3046https://doi.org/10.1145/3539618.3591909Conversion rate (CVR) estimation aims to predict the probability of conversion event after a user has clicked an ad. Typically, online publisher has user browsing interests and click feedbacks, while demand-side advertising platform collects users' post-...
- research-articleJuly 2023
Poisoning Self-supervised Learning Based Sequential Recommendations
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 300–310https://doi.org/10.1145/3539618.3591751Self-supervised learning (SSL) has been recently applied to sequential recommender systems to provide high-quality user representations. However, while facilitating the learning process recommender systems, SSL is not without security threats: carefully ...
- research-articleJuly 2023
Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 1690–1699https://doi.org/10.1145/3539618.3591722Federated Recommender Systems (FedRecs) are considered privacy-preserving techniques to collaboratively learn a recommendation model without sharing user data. Since all participants can directly influence the systems by uploading gradients, FedRecs are ...