204 citations · 238 across the 10 of their papers we have counts for
4 papers · 1 filter
Static Pruning in Dense Retrieval using Matrix Decomposition
Federico Siciliano, Francesca Pezzuti, Nicola Tonellotto +1
In the era of dense retrieval, document indexing and retrieval is largely based on encoding models that transform text documents into embeddings. The efficiency of retrieval is dir…
A Theoretical Analysis of Recommendation Loss Functions under Negative Sampling
Giulia Di Teodoro, Federico Siciliano, Nicola Tonellotto +1
Loss functions like Categorical Cross Entropy (CCE), Binary Cross Entropy (BCE), and Bayesian Personalized Ranking (BPR) are commonly used in training Recommender Systems (RSs) to…
A Reproducible Analysis of Sequential Recommender Systems
Filippo Betello, Antonio Purificato, Federico Siciliano +4
Sequential Recommender Systems (SRSs) have emerged as a highly efficient approach to recommendation systems. By leveraging sequential data, SRSs can identify temporal patterns in u…
The Power of Noise: Redefining Retrieval for RAG Systems
Florin Cuconasu, Giovanni Trappolini, Federico Siciliano +5
Retrieval-Augmented Generation (RAG) has recently emerged as a method to extend beyond the pre-trained knowledge of Large Language Models by augmenting the original prompt with rel…