Papers with utilization

2 papers
HSUGA: LLM-Enhanced Recommendation with Hierarchical Semantic Understanding and Group-Aware Alignment (2026.findings-acl)

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Challenge: Existing methods for enhancing sequential recommendation use long interaction sequences, but they lack the ability to extract user preferences from long sequences.
Approach: They propose a plugin that integrates LLMs to infer user preferences from interaction sequences.
Outcome: The proposed algorithms improve user semantic embedding extraction and utilization on three benchmark datasets.
SKRAG: A Retrieval-Augmented Generation Framework Guided by Reasoning Skeletons over Knowledge Graphs (2025.findings-emnlp)

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Challenge: Existing KG-based question answering frameworks face inefficient subgraph retrieval, limited reasoning capabilities, and high computational costs.
Approach: They propose a Skeleton-guided RAG framework for knowledge graph question answering . SKRAG leverages a lightweight language model enhanced with the Finite State Machine constraint .
Outcome: The proposed framework outperforms baselines and general-domain benchmarks on a KGQA dataset in the space science and utilization domain.

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