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Bridging Dense and Sparse Maximum Inner Product Search
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-$k$ retrieval in Information Retrieval.
An Approximate Algorithm for Maximum Inner Product Search over Streaming Sparse Vectors
To achieve optimal memory footprint and query latency, they rely on the near stationarity of documents and on laws governing natural languages.
An Analysis of Fusion Functions for Hybrid Retrieval
In particular, we examine fusion by a convex combination (CC) of lexical and semantic scores, as well as the Reciprocal Rank Fusion (RRF) method, and identify their advantages and potential pitfalls.
SDR: Efficient Neural Re-ranking using Succinct Document Representation
BERT based ranking models have achieved superior performance on various information retrieval tasks.
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