パーソナライズされた旅行推薦のためのディープニューラル協調フィルタリングモデル

Developing personalized travel recommendation systems is significant for better user experiences. This work introduces an advanced model using Neural Collaborative Filtering (NCF) to address diverse user preferences in travel planning. Traditional collaborative filtering struggles with data sparsity, cold start, and less common items, leading to suboptimal predictions. Our novel NCF model, learned by a neural network, overcomes these limitations and captures complex user-travel relationships. Employing a multi-layer perceptron, it refines predictions based on past interactions and real-time behavior updates. Experiments on real travel data show NCF significantly outperforms traditional methods in accuracy and user satisfaction, advancing personalized travel recommendation by effectively handling data sparsity and preference diversity.