Implementation of Personalized Information Recommendation Platform System Based on Deep Learning Tourism

In order to provide tourists with better tourism services, a system method of personal information recommendation platform based on deep learning is proposed. The includes noise reduction autoencoder, feature extraction module, data preprocessing calculation expert evaluation result output customer feedback and storage module. the present invention enables obtain conveniently quickly through scientific organization presentation form helps arrange plans decisions. By effectively aggregating multiple neighborhoods nodes, embedding high-order collaboration into node vector, obtaining potential preferences users, solving problems user sparse cold start, finally experimental analysis, research It used build model tourist attraction system. Experimental results show that proposed for cold-start has best performance in terms accuracy, recall, normalized loss cumulative gain, it 17.9% higher than BPR recall rate Recall@5 11.8% accuracy rate. proved significant impact diversity novelty recommendation.