Titlebook: Database Systems for Advanced Applications; 26th International C Christian S. Jensen,Ee-Peng Lim,Chih-Ya Shen Conference proceedings 2021 T

Stagger

可卡

Knowledge-Aware Hypergraph Neural Network for Recommender Systemsbased neighbors in hyperedge efficiently. Moreover, it can conduct the embedding propagation of high-order correlations explicitly and efficiently in knowledge-aware hypergraph. Finally, we apply the proposed model on three real-world datasets, and the empirical results demonstrate that KHNN can ach

Concrete

Personalized Dynamic Knowledge-Aware Recommendation with Hybrid Explanations novel interval-aware Gated Recurrent Unit (GRU). Furthermore, by leveraging self-attention mechanism, we can not only learn each review’s user-specific importance, but also provide tailored explanations for each user at both feature level and review level. We conduct extensive experiments on three

友好關(guān)系

Learning Disentangled User Representation Based on Controllable VAE for Recommendationon related to the real-world concepts using a factorized Gaussian distribution. Experimental results show that our model can get a crucial improvement over state-of-the-art baselines. We further evaluate our model’s effectiveness to control the trade-off between reconstruction error and disentanglem

Unsaturated-Fat

venous-leak

Tell Me Where to Go Next: Improving POI Recommendation via Conversationat the next turn to achieve successful POI recommendation within as few turns as possible. Finally, our extensive experiments on two real-world datasets demonstrate significant improvements over the state-of-the-art methods.

調(diào)整

MISS: A Multi-user Identification Network for Shared-Account Session-Aware Recommendationork to capture user interest drift across sessions. The other is a Multi-user Identification (MI) module, which draws on the attention mechanism to distinguish behaviors of different users under the same account. To verify the effectiveness of MISS, we construct two data sets with shared account cha

dowagers-hump

VizGRank: A Context-Aware Visualization Recommendation Method Based on Inherent Relations Between Viot reflect the realistic scenarios of visualization recommendation completely, a new benchmark for visualization recommendation is designed and constructed by collecting real public datasets. Extensive experiments on both the public benchmark and the new benchmark demonstrate that the VizGRank can b

Contort

Deep User Representation Construction Model for Collaborative Filteringtem for different users, which may limit the expressiveness and further improvement of the models. In this paper, we propose Deep User Representation Construction Model (DURCM) to construct user presentations in a more effective and robust way. Specially, different from existing item-item methods th

BOOM

DiCGAN: A Dilated Convolutional Generative Adversarial Network for Recommender Systems before convolution to generate attention maps for adaptive feature refinement. Experiments on several public datasets verify the superiority of DiCGAN over several baselines in terms of top-. recommendation. Further more, our experimental results show that when the dataset is more large and sparse,