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Found 6 papers, 1 papers with code
Preference Discerning with LLM-Enhanced Generative Retrieval
We assess current state-of-the-art methods using our benchmark and show that they struggle to accurately discern user preferences.
APOLLO: SGD-like Memory, AdamW-level Performance
This memory burden necessitates using more or higher-end GPUs or reducing batch sizes, limiting training scalability and throughput.
Unifying Generative and Dense Retrieval for Sequential Recommendation
This hybrid approach provides insights into the trade-offs between these approaches and demonstrates improvements in efficiency and effectiveness for recommendation systems in small-scale benchmarks.
MultiBalance: Multi-Objective Gradient Balancing in Industrial-Scale Multi-Task Recommendation System
In industrial recommendation systems, multi-task learning (learning multiple tasks simultaneously on a single model) is a predominant approach to save training/serving resources and improve recommendation performance via knowledge transfer between the joint 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.
