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Found 9 papers, 3 papers with code
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.
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.
Multi-channel Integrated Recommendation with Exposure Constraints
Integrated recommendation, which aims at jointly recommending heterogeneous items from different channels in a main feed, has been widely applied to various online platforms.
Cold-Start based Multi-Scenario Ranking Model for Click-Through Rate Prediction
Online travel platforms (OTPs), e. g., Ctrip. com or Fliggy. com, can effectively provide travel-related products or services to users.
Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search
Specifically, SIE is proposed to extract user's short-term interests by integrating three fundamental interests encoders within it namely query-dependent, target-dependent and causal-dependent interest encoder, respectively, followed by delivering the resultant representation to the module LIE, where it can effectively capture user long-term interests by devising an attention mechanism with respect to the short-term interests from SIE module.
Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation
In many classical e-commerce platforms, personalized recommendation has been proven to be of great business value, which can improve user satisfaction and increase the revenue of platforms.
SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios
In this paper, we propose a novel Scenario-Aware Ranking Network (SAR-Net) to address these issues.
A Linkage-based Doubly Imbalanced Graph Learning Framework for Face Clustering
Although imbalance problem has been extensively studied, the impact of imbalanced data on GCN- based linkage prediction task is quite different, which would cause problems in two aspects: imbalanced linkage labels and biased graph representations.
