Publications (43)
Hierarchical Neural Network for Extracting Knowledgeable Snippets and Documents
Ganbin Zhou, Rongyu Cao, Xiang Ao +4
In this study, we focus on extracting knowledgeable snippets and annotating knowledgeable documents from Web corpus, consisting of the documents from social media and We-media. Inf…
UPRec: User-Aware Pre-training for Recommender Systems
Chaojun Xiao, Ruobing Xie, Yuan Yao +4
Existing sequential recommendation methods rely on large amounts of training data and usually suffer from the data sparsity problem. To tackle this, the pre-training mechanism has…
Neural Snowball for Few-Shot Relation Learning
Tianyu Gao, Xu Han, Ruobing Xie +4
Knowledge graphs typically undergo open-ended growth of new relations. This cannot be well handled by relation extraction that focuses on pre-defined relations with sufficient trai…
Better Pre-Training by Reducing Representation Confusion
Haojie Zhang, Mingfei Liang, Ruobing Xie +3
In this work, we revisit the Transformer-based pre-trained language models and identify two different types of information confusion in position encoding and model representations,…
ID-centric Pre-training for Recommendation
Yiqing Wu, Ruobing Xie, Zhao Zhang +5
Classical sequential recommendation models generally adopt ID embeddings to store knowledge learned from user historical behaviors and represent items. However, these unique IDs ar…
Selective Fairness in Recommendation via Prompts
Yiqing Wu, Ruobing Xie, Yongchun Zhu +5
Recommendation fairness has attracted great attention recently. In real-world systems, users usually have multiple sensitive attributes (e.g. age, gender, and occupation), and user…
Multi-Granularity Click Confidence Learning via Self-Distillation in Recommendation
Chong Liu, Xiaoyang Liu, Lixin Zhang +2
Recommendation systems rely on historical clicks to learn user interests and provide appropriate items. However, current studies tend to treat clicks equally, which may ignore the…
DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback
Yiqing Wu, Ruobing Xie, Zhao Zhang +5
The graph-based recommendation has achieved great success in recent years. However, most existing graph-based recommendations focus on capturing user preference based on positive e…
Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect
Zheni Zeng, Chaojun Xiao, Yuan Yao +5
Recommender systems aim to provide item recommendations for users, and are usually faced with data sparsity problem (e.g., cold start) in real-world scenarios. Recently pre-trained…
CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation
Chong Liu, Xiaoyang Liu, Rongqin Zheng +6
Sequential recommendation methods are increasingly important in cutting-edge recommender systems. Through leveraging historical records, the systems can capture user interests and…
Algorithms vs. Peers: Shaping Engagement with Novel Content
Shan Huang, Yi Ji, Leyu Lin
The pervasive rise of digital platforms has reshaped how individuals engage with information, with algorithms and peer influence playing pivotal roles in these processes. This stud…
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach
Junjie Zhang, Ruobing Xie, Yupeng Hou +3
In the past decades, recommender systems have attracted much attention in both research and industry communities, and a large number of studies have been devoted to developing effe…
Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks
Yongchun Zhu, Ruobing Xie, Fuzhen Zhuang +5
Recently, embedding techniques have achieved impressive success in recommender systems. However, the embedding techniques are data demanding and suffer from the cold-start problem.…
Prompt Tuning for Discriminative Pre-trained Language Models
Yuan Yao, Bowen Dong, Ao Zhang +6
Recent works have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing (NLP) tasks. However, to the best of ou…
Real-time Attention Based Look-alike Model for Recommender System
Yudan Liu, Kaikai Ge, Xu Zhang +1
Recently, deep learning models play more and more important roles in contents recommender systems. However, although the performance of recommendations is greatly improved, the "Ma…
Personalized Prompt for Sequential Recommendation
Yiqing Wu, Ruobing Xie, Yongchun Zhu +4
Pre-training models have shown their power in sequential recommendation. Recently, prompt has been widely explored and verified for tuning in NLP pre-training, which could help to…
Atom Responding Machine for Dialog Generation
Ganbin Zhou, Ping Luo, Jingwu Chen +3
Recently, improving the relevance and diversity of dialogue system has attracted wide attention. For a post x, the corresponding response y is usually diverse in the real-world cor…
Incorporating Chinese Characters of Words for Lexical Sememe Prediction
Huiming Jin, Hao Zhu, Zhiyuan Liu +4
Sememes are minimum semantic units of concepts in human languages, such that each word sense is composed of one or multiple sememes. Words are usually manually annotated with their…
Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender
Shuai Yang, Lixin Zhang, Feng Xia +1
There are roughly three stages in real industrial recommendation systems, candidates generation (retrieval), ranking and reranking. Individual-level diversity and system-level dive…
Plug-in Diffusion Model for Sequential Recommendation
Haokai Ma, Ruobing Xie, Lei Meng +4
Pioneering efforts have verified the effectiveness of the diffusion models in exploring the informative uncertainty for recommendation. Considering the difference between recommend…
Contrastive Cross-domain Recommendation in Matching
Ruobing Xie, Qi Liu, Liangdong Wang +3
Cross-domain recommendation (CDR) aims to provide better recommendation results in the target domain with the help of the source domain, which is widely used and explored in real-w…
Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising
Yongchun Zhu, Yudan Liu, Ruobing Xie +6
In recommender systems and advertising platforms, marketers always want to deliver products, contents, or advertisements to potential audiences over media channels such as display,…
Long Short-Term Temporal Meta-learning in Online Recommendation
Ruobing Xie, Yalong Wang, Rui Wang +4
An effective online recommendation system should jointly capture users' long-term and short-term preferences in both users' internal behaviors (from the target recommendation task)…
Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network
Ruobing Xie, Qi Liu, Shukai Liu +4
Recently, real-world recommendation systems need to deal with millions of candidates. It is extremely challenging to conduct sophisticated end-to-end algorithms on the entire corpu…
Language Modeling with Sparse Product of Sememe Experts
Yihong Gu, Jun Yan, Hao Zhu +5
Most language modeling methods rely on large-scale data to statistically learn the sequential patterns of words. In this paper, we argue that words are atomic language units but no…
Personalized Transfer of User Preferences for Cross-domain Recommendation
Yongchun Zhu, Zhenwei Tang, Yudan Liu +5
Cold-start problem is still a very challenging problem in recommender systems. Fortunately, the interactions of the cold-start users in the auxiliary source domain can help cold-st…
UFNRec: Utilizing False Negative Samples for Sequential Recommendation
Xiaoyang Liu, Chong Liu, Pinzheng Wang +5
Sequential recommendation models are primarily optimized to distinguish positive samples from negative ones during training in which negative sampling serves as an essential compon…
Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction
Wen Wang, Wei Zhang, Shukai Liu +4
Session-based target behavior prediction aims to predict the next item to be interacted with specific behavior types (e.g., clicking). Although existing methods for session-based b…
Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidence
Ruobing Xie, Zhiyuan Liu, Fen Lin +1
Knowledge graphs (KGs), which could provide essential relational information between entities, have been widely utilized in various knowledge-driven applications. Since the overall…
AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems
Junjie Zhang, Yupeng Hou, Ruobing Xie +5
Recently, there has been an emergence of employing LLM-powered agents as believable human proxies, based on their remarkable decision-making capability. However, existing studies m…
Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users
Yongchun Zhu, Kaikai Ge, Fuzhen Zhuang +5
Cold-start problems are enormous challenges in practical recommender systems. One promising solution for this problem is cross-domain recommendation (CDR) which leverages rich info…
A Unified Framework for Community Detection and Network Representation Learning
Cunchao Tu, Xiangkai Zeng, Hao Wang +5
Network representation learning (NRL) aims to learn low-dimensional vectors for vertices in a network. Most existing NRL methods focus on learning representations from local contex…
Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback
Guipeng Xv, Xinyu Li, Ruobing Xie +5
Multi-modal recommender systems (MRSs) are pivotal in diverse online web platforms and have garnered considerable attention in recent years. However, previous studies overlook the…
COSINE: Compressive Network Embedding on Large-scale Information Networks
Zhengyan Zhang, Cheng Yang, Zhiyuan Liu +4
There is recently a surge in approaches that learn low-dimensional embeddings of nodes in networks. As there are many large-scale real-world networks, it's inefficient for existing…
FAQ-based Question Answering via Knowledge Anchors
Ruobing Xie, Yanan Lu, Fen Lin +1
Question answering (QA) aims to understand questions and find appropriate answers. In real-world QA systems, Frequently Asked Question (FAQ) based QA is usually a practical and eff…
Learning from All Sides: Diversified Positive Augmentation via Self-distillation in Recommendation
Chong Liu, Xiaoyang Liu, Ruobing Xie +3
Personalized recommendation relies on user historical behaviors to provide user-interested items, and thus seriously struggles with the data sparsity issue. A powerful positive ite…
Triple Sequence Learning for Cross-domain Recommendation
Haokai Ma, Ruobing Xie, Lei Meng +4
Cross-domain recommendation (CDR) aims to leverage the correlation of users' behaviors in both the source and target domains to improve the user preference modeling in the target d…
Denoising Relation Extraction from Document-level Distant Supervision
Chaojun Xiao, Yuan Yao, Ruobing Xie +5
Distant supervision (DS) has been widely used to generate auto-labeled data for sentence-level relation extraction (RE), which improves RE performance. However, the existing succes…
Multi-granularity Item-based Contrastive Recommendation
Ruobing Xie, Zhijie Qiu, Bo Zhang +1
Contrastive learning (CL) has shown its power in recommendation. However, most CL-based recommendation models build their CL tasks merely focusing on the user's aspects, ignoring t…
Curriculum-scheduled Knowledge Distillation from Multiple Pre-trained Teachers for Multi-domain Sequential Recommendation
Wenqi Sun, Ruobing Xie, Junjie Zhang +3
Pre-trained recommendation models (PRMs) have received increasing interest recently. However, their intrinsically heterogeneous model structure, huge model size and computation cos…
Reweighting Clicks with Dwell Time in Recommendation
Ruobing Xie, Lin Ma, Shaoliang Zhang +2
The click behavior is the most widely-used user positive feedback in recommendation. However, simply considering each click equally in training may suffer from clickbaits and title…
Understanding WeChat User Preferences and "Wow" Diffusion
Fanjin Zhang, Jie Tang, Xueyi Liu +9
WeChat is the largest social instant messaging platform in China, with 1.1 billion monthly active users. "Top Stories" is a novel friend-enhanced recommendation engine in WeChat, i…
Multi-view Multi-behavior Contrastive Learning in Recommendation
Yiqing Wu, Ruobing Xie, Yongchun Zhu +6
Multi-behavior recommendation (MBR) aims to jointly consider multiple behaviors to improve the target behavior's performance. We argue that MBR models should: (1) model the coarse-…