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Found 10 papers, 0 papers with code
Real-time Ad retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising
The integration of Large Language Models (LLMs) with retrieval systems has shown promising potential in retrieving documents (docs) or advertisements (ads) for a given query.
Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation
To the best of our knowledge, this is the first work to jointly optimize GNN architecture and behavior data importance for cross-domain recommendation.
CAdam: Confidence-Based Optimization for Online Learning
Modern recommendation systems frequently employ online learning to dynamically update their models with freshly collected data.
AdaS&S: a One-Shot Supernet Approach for Automatic Embedding Size Search in Deep Recommender System
To address these challenges, we propose a novel one-shot AES framework called AdaS&S, in which a supernet encompassing various candidate embeddings is built and AES is performed as searching network architectures within it.
Exploring the Potential of Large Language Models in Graph Generation
In this paper, we propose LLM4GraphGen to explore the ability of LLMs for graph generation with systematical task designs and extensive experiments.
FlexHB: a More Efficient and Flexible Framework for Hyperparameter Optimization
Comprehensive study on FlexHB shows that (1) our fine-grained fidelity method considerably enhances the efficiency of searching optimal configurations, (2) our FlexBand framework (self-adaptive allocation of SH brackets, and global ranking of configurations in both current and past SH procedures) grants the algorithm with more flexibility and improves the anytime performance.
LLM4VG: Large Language Models Evaluation for Video Grounding
Recently, researchers have attempted to investigate the capability of LLMs in handling videos and proposed several video LLM models.
A Survey on Dropout Methods and Experimental Verification in Recommendation
Among various methods of coping with overfitting, dropout is one of the representative ways.
Distributed Equivalent Substitution Training for Large-Scale Recommender Systems
We present Distributed Equivalent Substitution (DES) training, a novel distributed training framework for large-scale recommender systems with dynamic sparse features.
Neural Machine Translation with Key-Value Memory-Augmented Attention
Although attention-based Neural Machine Translation (NMT) has achieved remarkable progress in recent years, it still suffers from issues of repeating and dropping translations.
