1.5k citations · 1.8k across the 30 of their papers we have counts for
30 papers
Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
Xu Huang, Jianxun Lian, Yuxuan Lei +3
Recommender models excel at providing domain-specific item recommendations by leveraging extensive user behavior data. Despite their ability to act as lightweight domain experts, t…
Distillation from Heterogeneous Models for Top-K Recommendation
SeongKu Kang, Wonbin Kweon, Dongha Lee +3
Recent recommender systems have shown remarkable performance by using an ensemble of heterogeneous models. However, it is exceedingly costly because it requires resources and infer…
CDSM: Cascaded Deep Semantic Matching on Textual Graphs Leveraging Ad-hoc Neighbor Selection
Jing Yao, Zheng Liu, Junhan Yang +3
Deep semantic matching aims to discriminate the relationship between documents based on deep neural networks. In recent years, it becomes increasingly popular to organize documents…
Cooperative Retriever and Ranker in Deep Recommenders
Xu Huang, Defu Lian, Jin Chen +3
Deep recommender systems (DRS) are intensively applied in modern web services. To deal with the massive web contents, DRS employs a two-stage workflow: retrieval and ranking, to ge…
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation
Xinyan Fan, Jianxun Lian, Wayne Xin Zhao +3
A large-scale recommender system usually consists of recall and ranking modules. The goal of ranking modules (aka rankers) is to elaborately discriminate users' preference on item…
Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings
Shitao Xiao, Zheng Liu, Weihao Han +10
Vector quantization (VQ) based ANN indexes, such as Inverted File System (IVF) and Product Quantization (PQ), have been widely applied to embedding based document retrieval thanks…
Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval
Shitao Xiao, Zheng Liu, Weihao Han +9
Ad-hoc search calls for the selection of appropriate answers from a massive-scale corpus. Nowadays, the embedding-based retrieval (EBR) becomes a promising solution, where deep lea…
A Mutually Reinforced Framework for Pretrained Sentence Embeddings
Junhan Yang, Zheng Liu, Shitao Xiao +5
The lack of labeled data is a major obstacle to learning high-quality sentence embeddings. Recently, self-supervised contrastive learning (SCL) is regarded as a promising way to ad…
Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search
Jianjin Zhang, Zheng Liu, Weihao Han +9
Embedding based retrieval (EBR) is a fundamental building block in many web applications. However, EBR in sponsored search is distinguished from other generic scenarios and technic…
Reinforcement Routing on Proximity Graph for Efficient Recommendation
Chao Feng, Defu Lian, Xiting Wang +3
We focus on Maximum Inner Product Search (MIPS), which is an essential problem in many machine learning communities. Given a query, MIPS finds the most similar items with the maxim…
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…
Sampling-Decomposable Generative Adversarial Recommender
Binbin Jin, Defu Lian, Zheng Liu +4
Recommendation techniques are important approaches for alleviating information overload. Being often trained on implicit user feedback, many recommenders suffer from the sparsity c…
Self-supervised Graph Learning for Recommendation
Jiancan Wu, Xiang Wang, Fuli Feng +4
Representation learning on user-item graph for recommendation has evolved from using single ID or interaction history to exploiting higher-order neighbors. This leads to the succes…
Graph Convolution Machine for Context-aware Recommender System
Jiancan Wu, Xiangnan He, Xiang Wang +4
The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph. However, s…
KRED: Knowledge-Aware Document Representation for News Recommendations
Danyang Liu, Jianxun Lian, Shiyin Wang +4
News articles usually contain knowledge entities such as celebrities or organizations. Important entities in articles carry key messages and help to understand the content in a mor…