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Found 6 papers, 3 papers with code
Generating Long Semantic IDs in Parallel for Recommendation
Semantic ID-based recommendation models tokenize each item into a small number of discrete tokens that preserve specific semantics, leading to better performance, scalability, and memory efficiency.
Learning Graph Quantized Tokenizers
Graph Transformers (GTs) have recently emerged as leading models in geometric deep learning, outperforming Graph Neural Networks (GNNs) in various graph learning tasks.
How to Make LLMs Strong Node Classifiers?
Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs), in graph learning tasks.
EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations
Content-based recommendation systems play a crucial role in delivering personalized content to users in the digital world.
SPAR: Personalized Content-Based Recommendation via Long Engagement Attention
Leveraging users' long engagement histories is essential for personalized content recommendations.
A Hamiltonian Approach to Barrier Option Pricing Under Vasicek Model
In this paper, we study option pricing under Vasicek Model by a Hamiltonian approach.
