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- Zhejiang UniversityCN54 papers
- Peking UniversityCN29 papers
- Alibaba Group (United States)US27 papers
- Tsinghua UniversityCN25 papers
- Shanghai Jiao Tong UniversityCN20 papers
- University of Science and Technology of ChinaCN18 papers
- Chinese Academy of SciencesCN15 papers
- Wuhan UniversityCN14 papers
- Nanyang Technological UniversitySG12 papers
- University of Chinese Academy of SciencesCN11 papers
- Huazhong University of Science and TechnologyCN10 papers
- Hong Kong University of Science and TechnologyHK9 papers
9 papers · 2 filters
Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences
Qianying Lin, Wen-Ji Zhou, Yanshi Wang +3
Sequential recommendation predicts users' next behaviors with their historical interactions. Recommending with longer sequences improves recommendation accuracy and increases the d…
CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment
Rui Ma, Ning Liu, Jingsong Yuan +2
Traditional recommendation systems mainly focus on modeling user interests. However, the dynamics of recommended items caused by attribute modifications (e.g. changes in prices) ar…
Multi-level Contrastive Learning Framework for Sequential Recommendation
Ziyang Wang, Huoyu Liu, Wei Wei +5
Sequential recommendation (SR) aims to predict the subsequent behaviors of users by understanding their successive historical behaviors. Recently, some methods for SR are devoted t…
Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation
Yuanliang Zhang, Xiaofeng Wang, Jinxin Hu +3
Multi-scenario recommendation is dedicated to retrieve relevant items for users in multiple scenarios, which is ubiquitous in industrial recommendation systems. These scenarios enj…
Debiasing Learning for Membership Inference Attacks Against Recommender Systems
Zihan Wang, Na Huang, Fei Sun +5
Learned recommender systems may inadvertently leak information about their training data, leading to privacy violations. We investigate privacy threats faced by recommender systems…
Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System
Ding Zou, Wei Wei, Xian-Ling Mao +4
Knowledge graph (KG) plays an increasingly important role in recommender systems. Recently, graph neural networks (GNNs) based model has gradually become the theme of knowledge-awa…