GoSS-Rec: Group-Oriented Segment Sequence Recommendation

In recent years, the advancement of various applications, data mining, technologies, and socio-technical systems has led to the development of interactive platforms that enhance user experiences through personalization. In the sports domain, users can access training plans, routes and healthy habits, all in a personalized way thanks to sports recommender systems. These recommendation engines are fueled by rich datasets that are collected through continuous monitoring of users’ activities. However, their potential to address user profiling is limited to single users and not to the dynamics of groups of sportsmen. This paper introduces GoSS-Rec, a Group-oriented Segment Sequence Recommender System, which is designed for groups of cyclists who participate in fitness activities. The system analyzes collective preferences and activity records to provide personalized route recommendations that encourage exploration of diverse cycling paths and also enhance group activities. Our experiments show that GoSS-Rec, which is based on Prod2vec, consistently outperforms other models on diversity and novelty, regardless of the group size. This indicates the potential of our model to provide unique and customized suggestions, making GoSS-Rec a remarkable innovation in the field of sports recommender systems. It also expands the possibilities of personalized experiences beyond traditional areas.