A Multi-tiered Solution for Personalized Baggage Item Recommendations using FastText and Association Rule Mining

TL;DR

This paper presents a multi-tiered baggage recommendation system that combines FastText embeddings and Association Rule Mining to deliver personalized packing suggestions, improving travel experience and luggage management for air travelers.

Contribution

It introduces a novel integration of FastText and ARM for personalized baggage recommendations, enhancing packing efficiency and user satisfaction.

Findings

System effectively provides relevant, personalized baggage suggestions.

Improves luggage space utilization and compliance with weight limits.

Enhances customer satisfaction and simplifies packing process.

Abstract

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.