用认知类型+大模型解决冷启动推荐难题
Combating data scarcity in recommendation services: Integrating cognitive types of VARK and neural network technologies (LLM)
- 结合VARK认知类型与大模型，从少量数据生成用户画像
- 在MovieLens-1M上实现初始信息匮乏下的个性化推荐
- 适合需要解释性推荐的冷启动场景应用
冷启动问题严重制约推荐系统性能，尤其在用户无交互历史或物品元数据稀疏时。本文提出一种融合大语言模型（LLM）与VARK（视觉、听觉、读写、动觉）认知类型的混合框架。通过LLM进行内容语义分析与知识图谱构建，结合用户认知偏好进行动态画像，并根据心理状态调整展示形式。系统包含六部分：语义元数据增强、动态图构建、VARK画像、心理状态估计、图增强检索与LLM排序、自适应界面设计。在MovieLens-1M数据集上的实验表明，该框架可在初始信息有限情况下生成个性化推荐，为基于语义理解与心理建模的认知感知推荐系统奠定基础。
原文摘要 · Abstract (English)
Cold start scenarios present fundamental obstacles to effective recommendation generation, particularly when dealing with users lacking interaction history or items with sparse metadata. This research proposes an innovative hybrid framework that leverages Large Language Models (LLMs) for content semantic analysis and knowledge graph development, integrated with cognitive profiling based on VARK (Visual, Auditory, Reading/Writing, Kinesthetic) learning preferences. The proposed system tackles multiple cold start dimensions: enriching inadequate item descriptions through LLM processing, generating user profiles from minimal data, and dynamically adjusting presentation formats based on cognitive assessment. The framework comprises six integrated components: semantic metadata enhancement, dynamic graph construction, VARK-based profiling, mental state estimation, graph-enhanced retrieval with LLM-powered ranking, and adaptive interface design with iterative learning. Experimental validation on MovieLens-1M dataset demonstrates the system's capacity for personalized recommendation generation despite limited initial information. This work establishes groundwork for cognitively-aware recommendation systems capable of overcoming cold start limitations through semantic comprehension and psychological modeling, offering personalized, explainable recommendations from initial user contact.
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