基于用户行程与习惯，智能推荐行李物品，省时省力
A Multi-tiered Solution for Personalized Baggage Item Recommendations using FastText and Association Rule Mining
- 用FastText分析文本，结合用户搜索历史做个性化推荐
- 通过关联规则挖掘出常一起携带的物品组合，准确率高
- 适合旅行平台、航企优化打包服务，提升用户体验
本文提出一种智能行李物品推荐系统，旨在根据旅客行程与需求优化行李打包。系统采用预训练FastText词向量进行文本表示与相似性计算，结合用户搜索历史构建内容推荐模型，并利用关联规则挖掘（ARM）分析用户行为数据，发现高频共现物品组合。四阶段流程包括：数据收集与预处理、基于内容的推荐、关联规则应用及融合建模。评估指标涵盖覆盖率、支持度、置信度、提升度、杠杆率和确信度。实验结果表明，该系统能有效生成相关建议，提高客户满意度，简化打包过程，在航空出行及相关领域具有应用价值。
原文摘要 · Abstract (English)
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.
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