Research on Personalized Recommendation Algorithm for Smart Tourism Based on Deep Attention Interest Collaborative Filtering (DAICF)

This paper proposes a smart tourism personalized recommendation algorithm based on Deep Attention Interest Collaborative Filtering (DAICF), which solves the sparsity and cold-start problems in tourism recommendation by fusing geo-tagged photo data with deep learning techniques to mine explicit and implicit relationships of user interests.The research utilizes P-DBSCAN clustering algorithm to identify hotspot locations, constructs user-tour route binary association matrix, and designs deep collaborative filtering model, combining shallow linear interaction with deep nonlinear interaction, to synchronously mine the low-order and high-order relationships between user and item features.Based on the real dataset Yelp, DAICF performed well in the Top-N recommendation task: the Precision@5 was 20.13% and the Recall@15 was 35.26%, which was significantly higher than that of the baseline models such as EPT-GCN and ASGNN.In addition, the ablation experiments show that key components such as social relations and temporal context contribute prominently to the model performance.In practical application tests, the DAICF-generated travel routes shorten the total distance of the trip by 52.5% and improve the time-to-time ratio by 41.9%, which verifies its efficiency and practicality in trip planning.