39 citations · 57 across the 6 of their papers we have counts for
9 papers
Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1M
Dario Di Palma, Felice Antonio Merra, Maurizio Sfilio +3
Large Language Models (LLMs) have become increasingly central to recommendation scenarios due to their remarkable natural language understanding and generation capabilities. Althou…
Hyperband-based Bayesian Optimization for Black-box Prompt Selection
Lennart Schneider, Martin Wistuba, Aaron Klein +3
Optimal prompt selection is crucial for maximizing large language model (LLM) performance on downstream tasks, especially in black-box settings where models are only accessible via…
Formalizing Multimedia Recommendation through Multimodal Deep Learning
Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo +3
Recommender systems (RSs) offer personalized navigation experiences on online platforms, but recommendation remains a challenging task, particularly in specific scenarios and domai…
Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality
Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia +1
Recommender systems (RSs) employ user-item feedback, e.g., ratings, to match customers to personalized lists of products. Approaches to top-k recommendation mainly rely on Learning…
Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara +5
Recommender Systems have shown to be an effective way to alleviate the over-choice problem and provide accurate and tailored recommendations. However, the impressive number of prop…
Multi-Step Adversarial Perturbations on Recommender Systems Embeddings
Vito Walter Anelli, Alejandro Bellogín, Yashar Deldjoo +2
Recommender systems (RSs) have attained exceptional performance in learning users' preferences and helping them in finding the most suitable products. Recent advances in adversaria…