19 citations · 38 across the 8 of their papers we have counts for
6 papers · 1 filter
Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery Shopping
Ming Li, Mozhdeh Ariannezhad, Andrew Yates +1
Next basket recommendation (NBR) is the task of predicting the next set of items based on a sequence of already purchased baskets. It is a recommendation task that has been widely…
Generative Retrieval as Dense Retrieval
Thong Nguyen, Andrew Yates
Generative retrieval is a promising new neural retrieval paradigm that aims to optimize the retrieval pipeline by performing both indexing and retrieval with a single transformer m…
Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training
Yibin Lei, Liang Ding, Yu Cao +3
Dense retrievers have achieved impressive performance, but their demand for abundant training data limits their application scenarios. Contrastive pre-training, which constructs ps…
Adapting Learned Sparse Retrieval for Long Documents
Thong Nguyen, Sean MacAvaney, Andrew Yates
Learned sparse retrieval (LSR) is a family of neural retrieval methods that transform queries and documents into sparse weight vectors aligned with a vocabulary. While LSR approach…
A Unified Framework for Learned Sparse Retrieval
Thong Nguyen, Sean MacAvaney, Andrew Yates
Learned sparse retrieval (LSR) is a family of first-stage retrieval methods that are trained to generate sparse lexical representations of queries and documents for use with an inv…
Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval
Kai Hui, Andrew Yates, Klaus Berberich +1
Neural IR models, such as DRMM and PACRR, have achieved strong results by successfully capturing relevance matching signals. We argue that the context of these matching signals is…