Context-Aware Hybrid Filtering for Culinary Place Recommendation in Sukabumi

The growth of Sukabumi's culinary industry has significantly increased dining options, creating constraints for users in finding places that match personal preferences and situational conditions. This study develops a prototype of a culinary recommendation system based on Context-Aware Hybrid Filtering. The model integrates Content-Based Filtering (CBF), Singular Value Decomposition (SVD) Matrix Factorization, the Haversine Formula, and weather adaptation factors. Using the Cross-Industry Standard Process for Data Mining (CRISP-DM), the dataset was collected via web scraping from Google Maps, yielding 715 records with an extreme matrix sparsity of 94.81%. CBF was constructed using TF-IDF keyword extraction and cosine similarity, while SVD collaborative filtering used implicit feedback with a Confidence Alpha (α=40). The final ranking aggregates scores from CBF, SVD, popularity, ratings, distance, and current weather. The computational results demonstrated a consistent HitRate@10 and Recall@10 of 93.0%, proving that this hybrid architecture and dynamic context inclusion effectively generate relevant and personalized recommendations.