44 citations · 51 across the 5 of their papers we have counts for
5 papers
SERM: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams
Chenglong Wang, Canjia Li, Xingzhao Zhu +9
Due to the dynamically evolving nature of real-world query streams, relevance models struggle to generalize to practical search scenarios. A sophisticated solution is self-evolutio…
Pretrained Language Model based Web Search Ranking: From Relevance to Satisfaction
Canjia Li, Xiaoyang Wang, Dongdong Li +6
Search engine plays a crucial role in satisfying users' diverse information needs. Recently, Pretrained Language Models (PLMs) based text ranking models have achieved huge success…
Query-aware Tip Generation for Vertical Search
Yang Yang, Junmei Hao, Canjia Li +7
As a concise form of user reviews, tips have unique advantages to explain the search results, assist users' decision making, and further improve user experience in vertical search…
PARADE: Passage Representation Aggregation for Document Reranking
Canjia Li, Andrew Yates, Sean MacAvaney +2
Pretrained transformer models, such as BERT and T5, have shown to be highly effective at ad-hoc passage and document ranking. Due to inherent sequence length limits of these models…
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval
Canjia Li, Yingfei Sun, Ben He +5
Pseudo-relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new…