This paper introduces an intelligent baggage item recommendation system to optimize packing for air travelers by providing tailored suggestions based on specific travel needs and destinations. Using FastText word embeddings and Association Rule Mining (ARM), the system ensures efficient luggage space utilization, compliance with weight limits, and an enhanced travel experience. The methodology comprises four phases: (1) data collection and preprocessing with pre-trained FastText embeddings for text representation and similarity scoring (2) a content-based recommendation system enriched by user search history (3) application of ARM to user interactions to uncover meaningful item associations and (4) integration of FastText and ARM for accurate, personalized recommendations. Performance is evaluated using metrics such as coverage, support, confidence, lift, leverage, and conviction. Results demonstrate the system's effectiveness in providing relevant suggestions, improving customer satisfaction, and simplifying the packing process. These insights advance personalized recommendations, targeted marketing, and product optimization in air travel and beyond.

翻译：本文提出一种智能行李物品推荐系统，通过依据特定旅行需求与目的地提供定制化建议，以优化航空旅客的行李打包流程。该系统利用FastText词向量与关联规则挖掘技术，确保行李空间的高效利用、符合重量限制并提升旅行体验。方法框架包含四个阶段：(1) 采用预训练FastText嵌入进行文本表示与相似度评分的数椐收集与预处理；(2) 结合用户搜索历史增强的基于内容的推荐系统；(3) 应用关联规则挖掘分析用户交互以发现有效的物品关联模式；(4) 融合FastText与关联规则挖掘实现精准的个性化推荐。系统性能通过覆盖率、支持度、置信度、提升度、杠杆率与确信度等指标进行评估。实验结果表明，该系统能有效提供相关推荐、提升客户满意度并简化打包流程。相关研究成果对航空旅行及其他领域的个性化推荐、精准营销与产品优化具有推进意义。