Restaurant Recommendation Algorithm Based on Category Preferences and User Interests

Abstract

With the rapid development of the Internet and local life services, catering information has grown explosively, making it difficult for users to quickly find dining venues matching their preferences. To address information overload, homogeneous recommendations, and insufficient semantic understanding, this paper proposes a restaurant recommendation algorithm based on lightweight three-dimensional fine-grained semantic quantification and user interests. First, catering review texts are preprocessed, and a text representation model combining Word2Vec (word embedding) embeddings and TF-IDF (term frequency–inverse document frequency) weighting is constructed. An SVM (support vector machine) classifier is then used to categorize reviews into taste, environment, and service, enabling quantification and structuring of unstructured data. Next, user preference vectors and restaurant feature vectors are built from classification results. A hybrid strategy integrating cosine similarity and category label fitness is designed to generate a Top-5 personalized recommendation list. Experiments on 23,998 real reviews of Nanxin Dessert Shops show the SVM classifier achieves 90.7% overall accuracy, and the average comprehensive matching degree of recommendations is close to 0.9. The proposed method effectively mines users' fine-grained preferences, improves recommendation accuracy and personalization, and provides a feasible solution for catering platform recommendation systems.