Advances in Recommender Systems and Graph Learning

The field of recommender systems and graph learning is rapidly evolving, with a focus on improving the accuracy and diversity of recommendations. Recent developments have seen the integration of graph neural networks and multi-objective retrieval frameworks to balance semantic relevance and user engagement. Item-centric exploration and radial neighborhood smoothing are also being explored to address the cold start problem and improve recommendation efficiency. Furthermore, non-parametric graph convolution and self-loop iterative fusion of heterogeneous auxiliary information are being used to enhance ranking and recommendation performance. The use of contrastive cascade graph learning and test-time fast adaptation is also being investigated for classifying real and synthetic information diffusion patterns and predicting multi-scale information diffusion. Additionally, researchers are working on unifying self-supervised and supervised learning for graph recommendation and developing frameworks for fairness and diversity in recommender systems. Noteworthy papers include:

  • Balancing Semantic Relevance and Engagement in Related Video Recommendations, which introduces a novel multi-objective retrieval framework to enhance standard two-tower models.
  • Item-centric Exploration for Cold Start Problem, which introduces the concept of item-centric recommendations to identify the optimal users for new items.
  • SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation, which combines supervised and self-supervised learning into a cohesive supervised contrastive learning loss.

Sources

Graph Neural Network Enhanced Sequential Recommendation Method for Cross-Platform Ad Campaign

Balancing Semantic Relevance and Engagement in Related Video Recommendations

Item-centric Exploration for Cold Start Problem

Radial Neighborhood Smoothing Recommender System

Non-parametric Graph Convolution for Re-ranking in Recommendation Systems

SLIF-MR: Self-loop Iterative Fusion of Heterogeneous Auxiliary Information for Multimodal Recommendation

Large-Scale Graph Building in Dynamic Environments: Low Latency and High Quality

From Small to Large: A Graph Convolutional Network Approach for Solving Assortment Optimization Problems

Contrastive Cascade Graph Learning for Classifying Real and Synthetic Information Diffusion Patterns

Looking for Fairness in Recommender Systems

T3MAL: Test-Time Fast Adaptation for Robust Multi-Scale Information Diffusion Prediction

SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation

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