1.5k citations · 1.8k across the 30 of their papers we have counts for
10 papers · 1 filter
Semi-Supervised Variational User Identity Linkage via Noise-Aware Self-Learning
Chaozhuo Li, Senzhang Wang, Zheng Liu +3
User identity linkage, which aims to link identities of a natural person across different social platforms, has attracted increasing research interest recently. Existing approaches…
Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations
Yu Song, Jianxun Lian, Shuai Sun +4
User interest exploration is an important and challenging topic in recommender systems, which alleviates the closed-loop effects between recommendation models and user-item interac…
Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving
Tao Qi, Fangzhao Wu, Chuhan Wu +2
News recommendation is important for personalized online news services. Most existing news recommendation methods rely on centrally stored user behavior data to both train models o…
UserBERT: Contrastive User Model Pre-training
Chuhan Wu, Fangzhao Wu, Yang Yu +3
User modeling is critical for personalized web applications. Existing user modeling methods usually train user models from user behaviors with task-specific labeled data. However,…
GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph
Junhan Yang, Zheng Liu, Shitao Xiao +6
The representation learning on textual graph is to generate low-dimensional embeddings for the nodes based on the individual textual features and the neighbourhood information. Rec…
AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search
Chaozhuo Li, Bochen Pang, Yuming Liu +7
Sponsored search ads appear next to search results when people look for products and services on search engines. In recent years, they have become one of the most lucrative channel…
Hybrid Encoder: Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks
Junhan Yang, Zheng Liu, Bowen Jin +7
Transformer encoding networks have been proved to be a powerful tool of understanding natural languages. They are playing a critical role in native ads service, which facilitates t…
Matching-oriented Product Quantization For Ad-hoc Retrieval
Shitao Xiao, Zheng Liu, Yingxia Shao +2
Product quantization (PQ) is a widely used technique for ad-hoc retrieval. Recent studies propose supervised PQ, where the embedding and quantization models can be jointly trained…
Training Large-Scale News Recommenders with Pretrained Language Models in the Loop
Shitao Xiao, Zheng Liu, Yingxia Shao +2
News recommendation calls for deep insights of news articles' underlying semantics. Therefore, pretrained language models (PLMs), like BERT and RoBERTa, may substantially contribut…
Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations
Jianxun Lian, Iyad Batal, Zheng Liu +4
Precise user modeling is critical for online personalized recommendation services. Generally, users' interests are diverse and are not limited to a single aspect, which is particul…