Tourism Resources Recommendation for Self-Driving Tours Based on Group Decision-Making

In recent years, group self-driving tours have become a popular tour mode among Chinese tourists. However, current research on tourism resource recommendation generally overlooks the distinctive tourism styles and group preferences of self-driving tourists. To address the diversity in tourism styles and group preferences among self-driving tourists, this paper proposes a tourism resource recommendation method tailored for self-driving tours, based on tourism style modeling and group decision-making. First, by integrating tourist and tourism resource information, a knowledge graph of the self-driving tour environment is constructed, enhancing the accuracy of identifying similar tourists. Next, seven fundamental tourism types are incorporated along with factors specific to self-driving tours to better model the tourism styles of tourists, thereby improving recommendation accuracy. Furthermore, to accommodate the varied preferences within a self-driving group, a non-compensatory group decision-making strategy is employed to ensure the recommendation results meet the needs of all group members. Finally, case studies validate the effectiveness of the proposed method. This study can provide a new perspective for resource recommendation in self-driving tours.