12 citations · 29 across the 10 of their papers we have counts for
4 papers · 1 filter
ECLIPSE: Contrastive Dimension Importance Estimation with Pseudo-Irrelevance Feedback for Dense Retrieval
Giulio D'Erasmo, Giovanni Trappolini, Nicola Tonellotto +1
Recent advances in Information Retrieval have leveraged high-dimensional embedding spaces to improve the retrieval of relevant documents. Moreover, the Manifold Clustering Hypothes…
Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbations
Filippo Betello, Federico Siciliano, Pushkar Mishra +1
Sequential Recommender Systems (SRSs) are widely employed to model user behavior over time. However, their robustness in the face of perturbations in training data remains a largel…
Integrating Item Relevance in Training Loss for Sequential Recommender Systems
Andrea Bacciu, Federico Siciliano, Nicola Tonellotto +1
Sequential Recommender Systems (SRSs) are a popular type of recommender system that learns from a user's history to predict the next item they are likely to interact with. However,…
The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples
Ziheng Chen, Fabrizio Silvestri, Jia Wang +2
Deep learning-based recommender systems have become an integral part of several online platforms. However, their black-box nature emphasizes the need for explainable artificial int…