86 citations
- University of Chinese Academy of SciencesCN3 papers
- Amsterdam University of the ArtsNL2 papers
- Beihang UniversityCN2 papers
- Beijing Academy of Artificial IntelligenceCN2 papers
- Chinese Academy of SciencesCN2 papers
- Shandong UniversityCN2 papers
- Tsinghua UniversityCN2 papers
- University of AmsterdamNL2 papers
- Alibaba Group (China)CN1 paper
- Beijing Jiaotong UniversityCN1 paper
- Beijing University of Posts and TelecommunicationsCN1 paper
- City University of Hong KongHK1 paper
7 papers · 1 filter
Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai
Zhichao Feng, Junjiie Xie, Kaiyuan Li +7
In the recommender system of Meituan Waimai, we are dealing with ever-lengthening user behavior sequences, which pose an increasing challenge to modeling user preference effectivel…
NEON: Living Needs Prediction System in Meituan
Xiaochong Lan, Chen Gao, Shiqi Wen +6
Living needs refer to the various needs in human's daily lives for survival and well-being, including food, housing, entertainment, etc. On life service platforms that connect user…
Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
Xin Xin, Xiangyuan Liu, Hanbing Wang +8
Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target…
M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation
Zepeng Huai, Yuji Yang, Mengdi Zhang +3
Cross-domain recommendation (CDR) is an effective way to alleviate the data sparsity problem. Content-based CDR is one of the most promising branches since most kinds of products c…
Large-scale Multi-granular Concept Extraction Based on Machine Reading Comprehension
Siyu Yuan, Deqing Yang, Jiaqing Liang +5
The concepts in knowledge graphs (KGs) enable machines to understand natural language, and thus play an indispensable role in many applications. However, existing KGs have the poor…
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…