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Next-generation database interfaces: A survey of llm-based text-to-sql
Generating accurate SQL from natural language questions (text-to-SQL) is a long-standing
challenge due to the complexities in user question understanding, database schema …
challenge due to the complexities in user question understanding, database schema …
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 …
standing challenge for Graph Neural Networks (GNNs) due to the overwhelming …
Generative adversarial framework for cold-start item recommendation
The cold-start problem has been a long-standing issue in recommendation. Embedding-
based recommendation models provide recommendations by learning embeddings for each …
based recommendation models provide recommendations by learning embeddings for each …
Neighbor enhanced graph convolutional networks for node classification and recommendation
Abstract The recently proposed Graph Convolutional Networks (GCNs) have achieved
significantly superior performance on various graph-related tasks, such as node …
significantly superior performance on various graph-related tasks, such as node …
Multi-behavior collaborative filtering with partial order graph convolutional networks
Representing information of multiple behaviors in the single graph collaborative filtering
(CF) vector has been a long-standing challenge. This is because different behaviors …
(CF) vector has been a long-standing challenge. This is because different behaviors …
Graph Cross-Correlated Network for Recommendation
Collaborative filtering (CF) models have demonstrated remarkable performance in
recommender systems, which represent users and items as embedding vectors. Recently …
recommender systems, which represent users and items as embedding vectors. Recently …
Non-recursive cluster-scale graph interacted model for click-through rate prediction
Extracting users' interests from their behavior, particularly their 1-hop neighbors, has been
shown to enhance Click-Through Rate (CTR) prediction performance. However, online …
shown to enhance Click-Through Rate (CTR) prediction performance. However, online …
Content-based graph reconstruction for cold-start item recommendation
Graph convolutions have been successfully applied to recommendation systems, utilizing
high-order collaborative signals present in the user-item interaction graph. This idea …
high-order collaborative signals present in the user-item interaction graph. This idea …
Can graph neural networks go deeper without over-smoothing? Yes, with a randomized path exploration!
Graph Neural Networks (GNNs) have emerged as one of the most powerful approaches for
learning on graph-structured data, even though they are mostly restricted to being shallow in …
learning on graph-structured data, even though they are mostly restricted to being shallow in …
A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models
Large language models (LLMs) have demonstrated remarkable capabilities in a wide range
of tasks, yet their application to specialized domains remains challenging due to the need for …
of tasks, yet their application to specialized domains remains challenging due to the need for …