考虑上下文与多标准，提升群体推荐准确率
From Individual to Group: Developing a Context-Aware Multi-Criteria Group Recommender System
- 用多头注意力动态调整特征权重，融合上下文与多标准
- 在教育数据集上四种场景均优于现有方法
- 适合需要平衡多人偏好与情境的协作决策场景
群体决策在教育、餐饮、旅游和金融等领域日益普遍，需在多样个体偏好间协调。传统推荐系统因无法处理冲突偏好、上下文因素和多评估标准，在群体场景中表现不佳。本文提出上下文感知多标准群体推荐系统（CA-MCGRS），通过引入上下文信息和多标准评估机制，提升推荐准确性。模型采用多头注意力机制，动态权衡不同特征的重要性。在包含多种评分和上下文变量的教育数据集上，四类实验场景下CA-MCGRS均显著优于现有方法。研究结果表明，融合上下文与多标准评估对改进群体推荐至关重要，为构建更有效的群体推荐系统提供关键洞见。
原文摘要 · Abstract (English)
Group decision-making is becoming increasingly common in areas such as education, dining, travel, and finance, where collaborative choices must balance diverse individual preferences. While conventional recommender systems are effective in personalization, they fall short in group settings due to their inability to manage conflicting preferences, contextual factors, and multiple evaluation criteria. This study presents the development of a Context-Aware Multi-Criteria Group Recommender System (CA-MCGRS) designed to address these challenges by integrating contextual factors and multiple criteria to enhance recommendation accuracy. By leveraging a Multi-Head Attention mechanism, our model dynamically weighs the importance of different features. Experiments conducted on an educational dataset with varied ratings and contextual variables demonstrate that CA-MCGRS consistently outperforms other approaches across four scenarios. Our findings underscore the importance of incorporating context and multi-criteria evaluations to improve group recommendations, offering valuable insights for developing more effective group recommender systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。