416 citations · 1k across the 56 of their papers we have counts for
5 papers · 2 filters
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation
Ning Wu, Ming Gong, Linjun Shou +2
Online recommender systems (RS) aim to match user needs with the vast amount of resources available on various platforms. A key challenge is to model user preferences accurately un…
Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval
Shengyao Zhuang, Linjun Shou, Guido Zuccon
Effective cross-lingual dense retrieval methods that rely on multilingual pre-trained language models (PLMs) need to be trained to encompass both the relevance matching task and th…
Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval
Shengyao Zhuang, Linjun Shou, Jian Pei +4
Current dense retrievers (DRs) are limited in their ability to effectively process misspelled queries, which constitute a significant portion of query traffic in commercial search…
Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval
Houxing Ren, Linjun Shou, Ning Wu +2
In monolingual dense retrieval, lots of works focus on how to distill knowledge from cross-encoder re-ranker to dual-encoder retriever and these methods achieve better performance…
Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval
Houxing Ren, Linjun Shou, Jian Pei +3
Recent multilingual pre-trained models have shown better performance in various multilingual tasks. However, these models perform poorly on multilingual retrieval tasks due to lack…