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

2025-01-16

AI Summary

Existing luggage packing recommendation systems for air travelers suffer from insufficient personalization and struggle to jointly satisfy spatial and weight constraints. Method: This paper proposes a multi-layer recommendation framework integrating FastText semantic embeddings with Association Rule Mining (ARM). It jointly models user search behavior, destination-related textual semantics, and item co-occurrence patterns through multi-stage data fusion to achieve fine-grained demand understanding. Crucially, it pioneers the synergistic use of FastText word vectors and ARM metrics (support, confidence, lift) for luggage item recommendation—overcoming cold-start and long-tail limitations inherent in conventional collaborative filtering. Results: Experiments demonstrate significant improvements: +28.6% in recommendation coverage, +19.3% in NDCG@5, and a 37% increase in user packing efficiency. The framework delivers interpretable, deployable technical support for precision marketing and travel service optimization.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Smart Luggage Recommendation System
• Traveler's Destination
• Personal Needs Optimization

Innovation

• FastText
• Association Rule Mining
• Personalized Recommendation