CGSPA: Comprehensive Group Similarity Preference Aggregation Algorithm for Group Itinerary Recommendation System

One of the key factors to group multi-session recommendation system is about aggregating the preference of all users. To address the specific issues, we propose the Comprehensive Group Similarity Preference Aggregation (CGSPA) algorithm for Group Itinerary Recommendation System implemented in this paper. CGSPA comprehensively considers three aspects to aggregate group preference requirement, which are (1) the average maximum similarity between the recommendation plan and group preference; (2) the difference between the similarity of each session in the recommendation plan and the corresponding preference needs of the user group; (3) the historical similarity between the recommendation plan and group preference. We conduct extensive experiments to verify the preference aggregation performance and the robustness of CGSPA algorithm. We conclude that with different group scales, CGSPA’s group average satisfaction score is higher compared to the traditional preference aggregation algorithms.