108 citations · 444 across the 24 of their papers we have counts for
22 papers · 1 filter
Blending Learning to Rank and Dense Representations for Efficient and Effective Cascades
Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto +1
We investigate the exploitation of both lexical and neural relevance signals for ad-hoc passage retrieval. Our exploration involves a large-scale training dataset in which dense ne…
Efficient Recommendation with Millions of Items by Dynamic Pruning of Sub-Item Embeddings
Aleksandr V. Petrov, Craig Macdonald, Nicola Tonellotto
A large item catalogue is a major challenge for deploying modern sequential recommender models, since it makes the memory footprint of the model large and increases inference laten…
Efficient Inference of Sub-Item Id-based Sequential Recommendation Models with Millions of Items
Aleksandr V. Petrov, Craig Macdonald, Nicola Tonellotto
Transformer-based recommender systems, such as BERT4Rec or SASRec, achieve state-of-the-art results in sequential recommendation. However, it is challenging to use these models in…
A Reproducibility Study of PLAID
Sean MacAvaney, Nicola Tonellotto
The PLAID (Performance-optimized Late Interaction Driver) algorithm for ColBERTv2 uses clustered term representations to retrieve and progressively prune documents for final (exact…
DESIRE-ME: Domain-Enhanced Supervised Information REtrieval using Mixture-of-Experts
Pranav Kasela, Gabriella Pasi, Raffaele Perego +1
Open-domain question answering requires retrieval systems able to cope with the diverse and varied nature of questions, providing accurate answers across a broad spectrum of query…
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,…
A Static Pruning Study on Sparse Neural Retrievers
Carlos Lassance, Simon Lupart, Hervé Dejean +2
Sparse neural retrievers, such as DeepImpact, uniCOIL and SPLADE, have been introduced recently as an efficient and effective way to perform retrieval with inverted indexes. They a…
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…
Adaptive Re-Ranking with a Corpus Graph
Sean MacAvaney, Nicola Tonellotto, Craig Macdonald
Search systems often employ a re-ranking pipeline, wherein documents (or passages) from an initial pool of candidates are assigned new ranking scores. The process enables the use o…
Lecture Notes on Neural Information Retrieval
Nicola Tonellotto
These lecture notes focus on the recent advancements in neural information retrieval, with particular emphasis on the systems and models exploiting transformer networks. These netw…
Faster Learned Sparse Retrieval with Guided Traversal
Antonio Mallia, Joel Mackenzie, Torsten Suel +1
Neural information retrieval architectures based on transformers such as BERT are able to significantly improve system effectiveness over traditional sparse models such as BM25. Th…
On Approximate Nearest Neighbour Selection for Multi-Stage Dense Retrieval
Craig Macdonald, Nicola Tonellotto
Dense retrieval, which describes the use of contextualised language models such as BERT to identify documents from a collection by leveraging approximate nearest neighbour (ANN) te…
Query Embedding Pruning for Dense Retrieval
Nicola Tonellotto, Craig Macdonald
Recent advances in dense retrieval techniques have offered the promise of being able not just to re-rank documents using contextualised language models such as BERT, but also to us…
On Single and Multiple Representations in Dense Passage Retrieval
Craig Macdonald, Nicola Tonellotto, Iadh Ounis
The advent of contextualised language models has brought gains in search effectiveness, not just when applied for re-ranking the output of classical weighting models such as BM25,…
Pseudo-Relevance Feedback for Multiple Representation Dense Retrieval
Xiao Wang, Craig Macdonald, Nicola Tonellotto +1
Pseudo-relevance feedback mechanisms, from Rocchio to the relevance models, have shown the usefulness of expanding and reweighting the users' initial queries using information occu…
Learning Passage Impacts for Inverted Indexes
Antonio Mallia, Omar Khattab, Nicola Tonellotto +1
Neural information retrieval systems typically use a cascading pipeline, in which a first-stage model retrieves a candidate set of documents and one or more subsequent stages re-ra…
Declarative Experimentation in Information Retrieval using PyTerrier
Craig Macdonald, Nicola Tonellotto
The advent of deep machine learning platforms such as Tensorflow and Pytorch, developed in expressive high-level languages such as Python, have allowed more expressive representati…
Efficient Document Re-Ranking for Transformers by Precomputing Term Representations
Sean MacAvaney, Franco Maria Nardini, Raffaele Perego +3
Deep pretrained transformer networks are effective at various ranking tasks, such as question answering and ad-hoc document ranking. However, their computational expenses deem them…
Training Curricula for Open Domain Answer Re-Ranking
Sean MacAvaney, Franco Maria Nardini, Raffaele Perego +3
In precision-oriented tasks like answer ranking, it is more important to rank many relevant answers highly than to retrieve all relevant answers. It follows that a good ranking str…
Expansion via Prediction of Importance with Contextualization
Sean MacAvaney, Franco Maria Nardini, Raffaele Perego +3
The identification of relevance with little textual context is a primary challenge in passage retrieval. We address this problem with a representation-based ranking approach that:…
Topic Propagation in Conversational Search
I. Mele, C. I. Muntean, F. M. Nardini +3
In a conversational context, a user expresses her multi-faceted information need as a sequence of natural-language questions, i.e., utterances. Starting from a given topic, the con…
Topical Result Caching in Web Search Engines
Ida Mele, Nicola Tonellotto, Ophir Frieder +1
Caching search results is employed in information retrieval systems to expedite query processing and reduce back-end server workload. Motivated by the observation that queries belo…