112 citations · 386 across the 16 of their papers we have counts for
11 papers · 1 filter
Forward Index Compression for Learned Sparse Retrieval
Sebastian Bruch, Martino Fontana, Franco Maria Nardini +2
Text retrieval using learned sparse representations of queries and documents has, over the years, evolved into a highly effective approach to search. It is thanks to recent advance…
Investigating the Scalability of Approximate Sparse Retrieval Algorithms to Massive Datasets
Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli +2
Learned sparse text embeddings have gained popularity due to their effectiveness in top-k retrieval and inherent interpretability. Their distributional idiosyncrasies, however, hav…
Pairing Clustered Inverted Indexes with kNN Graphs for Fast Approximate Retrieval over Learned Sparse Representations
Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli +1
Learned sparse representations form an effective and interpretable class of embeddings for text retrieval. While exact top-k retrieval over such embeddings faces efficiency challen…
Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations
Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli +1
Learned sparse representations form an attractive class of contextual embeddings for text retrieval. That is so because they are effective models of relevance and are interpretable…
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search
Thomas Vecchiato, Claudio Lucchese, Franco Maria Nardini +1
A critical piece of the modern information retrieval puzzle is approximate nearest neighbor search. Its objective is to return a set of data points that are closest to a query…
Bridging Dense and Sparse Maximum Inner Product Search
Sebastian Bruch, Franco Maria Nardini, Amir Ingber +1
Maximum inner product search (MIPS) over dense and sparse vectors have progressed independently in a bifurcated literature for decades; the latter is better known as top- retrie…
Efficient and Effective Tree-based and Neural Learning to Rank
Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini
This monograph takes a step towards promoting the study of efficiency in the era of neural information retrieval by offering a comprehensive survey of the literature on efficiency…
An Approximate Algorithm for Maximum Inner Product Search over Streaming Sparse Vectors
Sebastian Bruch, Franco Maria Nardini, Amir Ingber +1
Maximum Inner Product Search or top-k retrieval on sparse vectors is well-understood in information retrieval, with a number of mature algorithms that solve it exactly. However, al…
An Analysis of Fusion Functions for Hybrid Retrieval
Sebastian Bruch, Siyu Gai, Amir Ingber
We study hybrid search in text retrieval where lexical and semantic search are fused together with the intuition that the two are complementary in how they model relevance. In part…
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank
Rama Kumar Pasumarthi, Sebastian Bruch, Xuanhui Wang +7
Learning-to-Rank deals with maximizing the utility of a list of examples presented to the user, with items of higher relevance being prioritized. It has several practical applicati…
Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks
Qingyao Ai, Xuanhui Wang, Sebastian Bruch +3
While in a classification or a regression setting a label or a value is assigned to each individual document, in a ranking setting we determine the relevance ordering of the entire…