484 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Shandong Academy of SciencesCN58 papers
- Shandong UniversityCN15 papers
- Shandong Normal UniversityCN13 papers
- National University of SingaporeSG9 papers
- University of MacauMO8 papers
- Changchun Institute of TechnologyCN7 papers
- Institute of Oceanographic InstrumentationCN7 papers
- Deakin UniversityAU6 papers
- Shandong University of Finance and EconomicsCN6 papers
- Chinese Academy of SciencesCN5 papers
- Monash UniversityAU5 papers
- Northeast Electric Power UniversityCN5 papers
11 papers · 1 filter
FedCRF: A Federated Cross-domain Recommendation Method with Semantic-driven Deep Knowledge Fusion
Lei Guo, Ting Yang, Xu Yu +3
As user behavior data becomes increasingly scattered across different platforms, achieving cross-domain knowledge fusion while preserving privacy has become a critical issue in rec…
Semantic-enhanced Co-attention Prompt Learning for Non-overlapping Cross-Domain Recommendation
Lei Guo, Chenlong Song, Feng Guo +3
Non-overlapping Cross-domain Sequential Recommendation (NCSR) is the task that focuses on domain knowledge transfer without overlapping entities. Compared with traditional Cross-do…
Multi-Behavior Recommendation with Cascading Graph Convolution Networks
Zhiyong Cheng, Sai Han, Fan Liu +3
Multi-behavior recommendation, which exploits auxiliary behaviors (e.g., click and cart) to help predict users' potential interactions on the target behavior (e.g., buy), is regard…
UGRec: Modeling Directed and Undirected Relations for Recommendation
Xinxiao Zhao, Zhiyong Cheng, Lei Zhu +2
Recommender systems, which merely leverage user-item interactions for user preference prediction (such as the collaborative filtering-based ones), often face dramatic performance d…
Enhancing Factorization Machines with Generalized Metric Learning
Yangyang Guo, Zhiyong Cheng, Jiazheng Jing +3
Factorization Machines (FMs) are effective in incorporating side information to overcome the cold-start and data sparsity problems in recommender systems. Traditional FMs adopt the…
Jointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation
Shoujin Wang, Longbing Cao, Liang Hu +4
A transaction-based recommender system (TBRS) aims to predict the next item by modeling dependencies in transactional data. Generally, two kinds of dependencies considered are intr…
User Diverse Preference Modeling by Multimodal Attentive Metric Learning
Fan Liu, Zhiyong Cheng, Changchang Sun +3
Most existing recommender systems represent a user's preference with a feature vector, which is assumed to be fixed when predicting this user's preferences for different items. How…
Compositional Coding for Collaborative Filtering
Chenghao Liu, Tao Lu, Xin Wang +3
Efficiency is crucial to the online recommender systems. Representing users and items as binary vectors for Collaborative Filtering (CF) can achieve fast user-item affinity computa…
Attentive Long Short-Term Preference Modeling for Personalized Product Search
Yangyang Guo, Zhiyong Cheng, Liqiang Nie +3
E-commerce users may expect different products even for the same query, due to their diverse personal preferences. It is well-known that there are two types of preferences: long-te…
MMALFM: Explainable Recommendation by Leveraging Reviews and Images
Zhiyong Cheng, Xiaojun Chang, Lei Zhu +2
Although the latent factor model achieves good accuracy in rating prediction, it suffers from many problems including cold-start, non-transparency, and suboptimal results for indiv…
Attentive Aspect Modeling for Review-aware Recommendation
Xinyu Guan, Zhiyong Cheng, Xiangnan He +4
In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user…