584 citations · 3.1k across the 56 of their papers we have counts for
10 papers · 2 filters
Whole Page Unbiased Learning to Rank
Haitao Mao, Lixin Zou, Yujia Zheng +5
The page presentation biases in the information retrieval system, especially on the click behavior, is a well-known challenge that hinders improving ranking models' performance wit…
Approximated Doubly Robust Search Relevance Estimation
Lixin Zou, Changying Hao, Hengyi Cai +6
Extracting query-document relevance from the sparse, biased clickthrough log is among the most fundamental tasks in the web search system. Prior art mainly learns a relevance judgm…
Model-based Unbiased Learning to Rank
Dan Luo, Lixin Zou, Qingyao Ai +3
Unbiased Learning to Rank (ULTR) that learns to rank documents with biased user feedback data is a well-known challenge in information retrieval. Existing methods in unbiased learn…
Hypergraph Contrastive Collaborative Filtering
Lianghao Xia, Chao Huang, Yong Xu +3
Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interact…
Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking
Qian Dong, Yiding Liu, Suqi Cheng +4
Passage re-ranking is to obtain a permutation over the candidate passage set from retrieval stage. Re-rankers have been boomed by Pre-trained Language Models (PLMs) due to their ov…
Graph Enhanced BERT for Query Understanding
Juanhui Li, Yao Ma, Wei Zeng +4
Query understanding plays a key role in exploring users' search intents and facilitating users to locate their most desired information. However, it is inherently challenging since…
Sequential Recommendation with User Evolving Preference Decomposition
Weiqi Shao, Xu Chen, Long Xia +2
Modeling user sequential behaviors has recently attracted increasing attention in the recommendation domain. Existing methods mostly assume coherent preference in the same sequence…
Contrastive Meta Learning with Behavior Multiplicity for Recommendation
Wei Wei, Chao Huang, Lianghao Xia +3
A well-informed recommendation framework could not only help users identify their interested items, but also benefit the revenue of various online platforms (e.g., e-commerce, soci…
User behavior understanding in real world settings
Weiqi Shao, Xu Chen, Jiashu Zhao +2
How to extract meaningful information in user historical behavior plays a crucial role in recommendation. User behavior sequence often contains multiple conceptually distinct items…
Gumble Softmax For User Behavior Modeling
Weiqi Shao, Xu Chen, Jiashu Zhao +2
Recently, sequential recommendation systems are important in solving the information overload in many online services. Current methods in sequential recommendation focus on learnin…