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Found 31 papers, 15 papers with code
Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search
As the fundamental basis of sponsored search, relevance modeling has attracted increasing attention due to the tremendous practical value.
FineFake: A Knowledge-Enriched Dataset for Fine-Grained Multi-Domain Fake News Detecction
Existing benchmarks for fake news detection have significantly contributed to the advancement of models in assessing the authenticity of news content.
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.
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems
Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative filtering (CF) through the modeling of user-item interaction graphs.
Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models
Machine learning has demonstrated remarkable performance over finite datasets, yet whether the scores over the fixed benchmarks can sufficiently indicate the model's performance in the real world is still in discussion.
ConvFormer: Revisiting Transformer for Sequential User Modeling
Sequential user modeling, a critical task in personalized recommender systems, focuses on predicting the next item a user would prefer, requiring a deep understanding of user behavior sequences.
Continual Learning on Dynamic Graphs via Parameter Isolation
Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs.
To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion
Embedding models have shown great power in knowledge graph completion (KGC) task.
Generative Sentiment Transfer via Adaptive Masking
Sentiment transfer aims at revising the input text to satisfy a given sentiment polarity while retaining the original semantic content.
Learning on Large-scale Text-attributed Graphs via Variational Inference
In this paper, we propose an efficient and effective solution to learning on large text-attributed graphs by fusing graph structure and language learning with a variational Expectation-Maximization (EM) framework, called GLEM.
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RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction
Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages.
Test-Time Training for Graph Neural Networks
To bridge such gap, in this work we introduce the first test-time training framework for GNNs to enhance the model generalization capacity for the graph classification task.
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.
Geometric Interaction Augmented Graph Collaborative Filtering
Graph-based collaborative filtering is capable of capturing the essential and abundant collaborative signals from the high-order interactions, and thus received increasingly research interests.
Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network
Based on this observation, we intuitively propose to remove the GNN propagation part, while the readout module will take on more responsibility in the model reasoning process.
Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation
Session-based recommendation (SBR) aims to predict the user next action based on the ongoing sessions.
Going Deeper into Permutation-Sensitive Graph Neural Networks
We prove that our approach is strictly more powerful than the 2-dimensional Weisfeiler-Lehman (2-WL) graph isomorphism test and not less powerful than the 3-WL test.
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation
We first extract distribution patterns from the item candidates.
AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation
Sequential recommendation (SR) aims to model users dynamic preferences from a series of interactions.
HousE: Knowledge Graph Embedding with Householder Parameterization
The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties.
Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval
In this work, we tackle this problem with Bi-Granular Document Representation, where the lightweight sparse embeddings are indexed and standby in memory for coarse-grained candidate search, and the heavyweight dense embeddings are hosted in disk for fine-grained post verification.
An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering
Thus, graph pre-training has the great potential to alleviate data sparsity in GNN-based recommendations.
Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation with Pre-training
Our approach leverages bidirectional temporal augmentation and knowledge-enhanced fine-tuning to synthesize authentic pseudo-prior items that \emph{retain user preferences and capture deeper item semantic correlations}, thus boosting the model's expressive power.
Gophormer: Ego-Graph Transformer for Node Classification
Existing graph transformer models typically adopt fully-connected attention mechanism on the whole input graph and thus suffer from severe scalability issues and are intractable to train in data insufficient cases.
Localized Graph Collaborative Filtering
These methods often make recommendations based on the learned user and item embeddings.
GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph
The representation learning on textual graph is to generate low-dimensional embeddings for the nodes based on the individual textual features and the neighbourhood information.
AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search
Our motivation lies in incorporating the tremendous amount of unsupervised user behavior data from the historical search logs as the complementary graph to facilitate relevance modeling.
Customized Graph Neural Networks
Therefore, in this paper, we aim to develop customized graph neural networks for graph classification.
Learning with Noisy Labels for Sentence-level Sentiment Classification
We propose a novel DNN model called NetAb (as shorthand for convolutional neural Networks with Ab-networks) to handle noisy labels during training.
Multi-Hot Compact Network Embedding
Specifically, we propose a MCNE model to learn compact embeddings from pre-learned node features.
Detecting Context Dependent Messages in a Conversational Environment
While automatic response generation for building chatbot systems has drawn a lot of attention recently, there is limited understanding on when we need to consider the linguistic context of an input text in the generation process.
