542 citations · 1.5k across the 79 of their papers we have counts for
22 papers · 1 filter
Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation
Liangcai Su, Fan Yan, Jieming Zhu +5
Two-tower models are a prevalent matching framework for recommendation, which have been widely deployed in industrial applications. The success of two-tower matching attributes to…
GMOCAT: A Graph-Enhanced Multi-Objective Method for Computerized Adaptive Testing
Hangyu Wang, Ting Long, Liang Yin +6
Computerized Adaptive Testing(CAT) refers to an online system that adaptively selects the best-suited question for students with various abilities based on their historical respons…
Diffusion Augmentation for Sequential Recommendation
Qidong Liu, Fan Yan, Xiangyu Zhao +4
Sequential recommendation (SRS) has become the technical foundation in many applications recently, which aims to recommend the next item based on the user's historical interactions…
Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation
Jingtong Gao, Bo Chen, Menghui Zhu +6
Click-Through Rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have shown that implementing multi-scenario recommendations…
HAMUR: Hyper Adapter for Multi-Domain Recommendation
Xiaopeng Li, Fan Yan, Xiangyu Zhao +4
Multi-Domain Recommendation (MDR) has gained significant attention in recent years, which leverages data from multiple domains to enhance their performance concurrently.However, cu…
Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction
Hengyu Zhang, Chang Meng, Wei Guo +5
Click-Through Rate (CTR) prediction, crucial in applications like recommender systems and online advertising, involves ranking items based on the likelihood of user clicks. User be…
Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System
Bowei He, Xu He, Renrui Zhang +3
With the continuous increase of users and items, conventional recommender systems trained on static datasets can hardly adapt to changing environments. The high-throughput data req…
AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation
Ziru Liu, Kecheng Chen, Fengyi Song +4
In the domain of streaming recommender systems, conventional methods for addressing new user IDs or item IDs typically involve assigning initial ID embeddings randomly. However, th…
MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction
Jianghao Lin, Yanru Qu, Wei Guo +4
With the widespread application of personalized online services, click-through rate (CTR) prediction has received more and more attention and research. The most prominent features…
ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation
Jianghao Lin, Rong Shan, Chenxu Zhu +6
With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, LLM-enhanced recommender systems have received much attention and…
Contrastive Multi-view Framework for Customer Lifetime Value Prediction
Chuhan Wu, Jingjie Li, Qinglin Jia +3
Accurate customer lifetime value (LTV) prediction can help service providers optimize their marketing policies in customer-centric applications. However, the heavy sparsity of cons…
Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation
Xianyu Chen, Jian Shen, Wei Xia +9
With the development of the online education system, personalized education recommendation has played an essential role. In this paper, we focus on developing path recommendation s…
Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models
Yunjia Xi, Weiwen Liu, Jianghao Lin +8
Recommender systems play a vital role in various online services. However, the insulated nature of training and deploying separately within a specific domain limits their access to…
ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop
Jieming Zhu, Guohao Cai, Junjie Huang +3
Industrial recommender systems face the challenge of operating in non-stationary environments, where data distribution shifts arise from evolving user behaviors over time. To tackl…
How Can Recommender Systems Benefit from Large Language Models: A Survey
Jianghao Lin, Xinyi Dai, Yunjia Xi +11
With the rapid development of online services, recommender systems (RS) have become increasingly indispensable for mitigating information overload. Despite remarkable progress, con…
CTRL: Connect Collaborative and Language Model for CTR Prediction
Xiangyang Li, Bo Chen, Lu Hou +1
Traditional click-through rate (CTR) prediction models convert the tabular data into one-hot vectors and leverage the collaborative relations among features for inferring the user'…
Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems
Yuening Wang, Yingxue Zhang, Antonios Valkanas +4
Recommender systems now consume large-scale data and play a significant role in improving user experience. Graph Neural Networks (GNNs) have emerged as one of the most effective re…
Dynamically Expandable Graph Convolution for Streaming Recommendation
Bowei He, Xu He, Yingxue Zhang +2
Personalized recommender systems have been widely studied and deployed to reduce information overload and satisfy users' diverse needs. However, conventional recommendation models…
Compressed Interaction Graph based Framework for Multi-behavior Recommendation
Wei Guo, Chang Meng, Enming Yuan +8
Multi-types of user behavior data (e.g., clicking, adding to cart, and purchasing) are recorded in most real-world recommendation scenarios, which can help to learn users' multi-fa…
REASONER: An Explainable Recommendation Dataset with Multi-aspect Real User Labeled Ground Truths Towards more Measurable Explainable Recommendation
Xu Chen, Jingsen Zhang, Lei Wang +6
Explainable recommendation has attracted much attention from the industry and academic communities. It has shown great potential for improving the recommendation persuasiveness, in…
A Survey on User Behavior Modeling in Recommender Systems
Zhicheng He, Weiwen Liu, Wei Guo +4
User Behavior Modeling (UBM) plays a critical role in user interest learning, which has been extensively used in recommender systems. Crucial interactive patterns between users and…
Multi-Task Deep Recommender Systems: A Survey
Yuhao Wang, Ha Tsz Lam, Yi Wong +8
Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual improvement among tasks considering their shared knowledge. It is an important topic i…