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Thrilled to announce that our full research paper, "Investigating the Robustness of Sequential Recommender Systems Under Training Data Perturbation" (co-authored by Filippo Betello, Federico Siciliano, Pushkar Mishra, and Fabrizio Silvestri), has been accepted at the ECIR 2024 conference. In our study, we delve into the effects of item positioning within chronologically ordered training sequences. Our findings reveal a substantial impact: removing the most recent items can degrade NDCG@20 by as much as 60%, and the resulting ranked lists of suggestions share merely 10% of the ground truth items. These insights underscore significant implications for real-world recommender system applications. Access the preprint version here https://arxiv.org/pdf/2307.13165v2.pdf to read the full details. Looking forward to seeing you in Glasgow 🥳!
We are at LoG meetup - Trento. Come and chat with us during the poster session about our works "Link Prediction with Graph Neural Networks" and "Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design". We'll be glad to talk with you.
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