72 citations · 320 across the 36 of their papers we have counts for
3 papers · 1 filter
Caching Historical Embeddings in Conversational Search
Ophir Frieder, Ida Mele, Cristina Ioana Muntean +3
Rapid response, namely low latency, is fundamental in search applications; it is particularly so in interactive search sessions, such as those encountered in conversational setting…
ILMART: Interpretable Ranking with Constrained LambdaMART
Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando +2
Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models. While most o…
Distilled Neural Networks for Efficient Learning to Rank
F. M. Nardini, C. Rulli, S. Trani +1
Recent studies in Learning to Rank have shown the possibility to effectively distill a neural network from an ensemble of regression trees. This result leads neural networks to bec…