Search Results for author:
Found 16 papers, 3 papers with code
High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text-attributed Graphs
Moreover, we show that existing contrastive objective learns the low-frequency component of the augmentation graph and propose a high-frequency component (HFC)-aware contrastive learning objective that makes the learned embeddings more distinctive.
Structure Guided Large Language Model for SQL Generation
Existing models typically input queries and database schemas into the LLM and rely on the LLM to perform semantic-structure matching and generate structured SQL.
Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM
Generating accurate SQL for user queries (text-to-SQL) is a long-standing problem since the generation of the SQL requires comprehending the query and database and retrieving the accurate data from the database accordingly.
Large Language Model Interaction Simulator for Cold-Start Item Recommendation
To address this challenge, we propose an LLM Interaction Simulator (LLM-InS) to model users' behavior patterns based on the content aspect.
Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks
POG defines the partial order relation of multiple behaviors and models behavior combinations as weighted edges to merge separate behavior graphs into a joint POG.
Macro Graph Neural Networks for Online Billion-Scale Recommender Systems
Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-standing challenge for Graph Neural Networks (GNNs) due to the overwhelming computational complexity involved in aggregating billions of neighbors.
Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering
To this end, we propose IMGCF, a simple but effective model to alleviate behavior data imbalance for multi-behavior graph collaborative filtering.
Multi-factor Sequential Re-ranking with Perception-Aware Diversification
Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications.
Flattened Graph Convolutional Networks For Recommendation
Third, we propose a layer ensemble technique which improves the expressiveness of the learned representations by assembling the layer-wise neighborhood representations at the final layer.
GPatch: Patching Graph Neural Networks for Cold-Start Recommendations
State-of-the-art solutions rely on training hybrid models for both cold-start and existing users/items, based on the auxiliary information.
EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud Computing
To this end, image-encryption-based privacy-preserving image retrieval schemes have been developed, which first extract features from cipher-images, and then build retrieval models based on these features.
Generative Adversarial Framework for Cold-Start Item Recommendation
Embedding-based recommendation models provide recommendations by learning embeddings for each user and item from historical interactions.
Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation
The experimental results verify that our proposed NEGCN framework can significantly enhance the performance for various typical GCN models on both node classification and recommendation tasks.
Hierarchical Cross-Modality Semantic Correlation Learning Model for Multimodal Summarization
In this paper, we propose a hierarchical cross-modality semantic correlation learning model (HCSCL) to learn the intra- and inter-modal correlation existing in the multimodal data.
Label-Aware Graph Convolutional Networks
In this paper, we consider the problem of node classification and propose the Label-Aware Graph Convolutional Network (LAGCN) framework which can directly identify valuable neighbors to enhance the performance of existing GCN models.
Learning Social Image Embedding with Deep Multimodal Attention Networks
Extensive experiments are conducted to investigate the effectiveness of our approach in the applications of multi-label classification and cross-modal search.
