Search Results for author:
Found 7 papers, 6 papers with code
DualVAE: Dual Disentangled Variational AutoEncoder for Recommendation
To address this problem, we propose a Dual Disentangled Variational AutoEncoder (DualVAE) for collaborative recommendation, which combines disentangled representation learning with variational inference to facilitate the generation of implicit interaction data.
LGMRec: Local and Global Graph Learning for Multimodal Recommendation
The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e. g., purchases, clicks) and item various modalities (e. g., visual and textual).
MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level Dependencies
Specifically, for neighborhood-level dependencies, we explicitly consider both popularity score and preference correlation by designing a joint neighborhood-level dependency weight, based on which we construct a neighborhood-level dependencies graph to capture higher-order interaction features.
TopicVAE: Topic-aware Disentanglement Representation Learning for Enhanced Recommendation
However, most existing methods considering content information are not well-designed to disentangle user preference features due to neglecting the diversity of user preference on different semantic topics of items, resulting in sub-optimal performance and low interpretability.
Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer's Disease Detection
Finally, a best detection accuracy of 81. 6{\%} is obtained by our proposed methods on the Mandarin AD corpus.
A Light Heterogeneous Graph Collaborative Filtering Model using Textual Information
Afterward, by performing a simplified RGCN-based node information propagation on the constructed heterogeneous graph, the embeddings of users and items can be adjusted with textual knowledge, which effectively alleviates the negative effects of data sparsity.
A Text-based Deep Reinforcement Learning Framework for Interactive Recommendation
IRSs usually face the large discrete action space problem, which makes most of the existing RL-based recommendation methods inefficient.
