Knowledge-based Context-aware Group Recommender System for Point of Interest recommendation

The rise of Location-based Social Networking (LBSN) platforms has transformed the way users explore Points of Interest (POIs), increasingly relying on group-based recommendations. However, recommending POIs to groups presents unique challenges due to conflicting preferences among members. Traditional group recommendation algorithms often prioritize aggregated methods or explicit preference extraction, overlooking latent domain-specific information and the dynamic nature of group decision-making. To address these gaps, we propose a novel Knowledge-based Context-Aware Group Recommender System (KCGRS) designed to support decision-making processes within groups. KCGRS operates in two key stages: first, it utilizes a knowledge graph to learn domain-specific embeddings for both users and POIs, ensuring that implicit preferences and contextual factors are incorporated. In the second stage, these embeddings are enhanced with contextual information using a feed-forward transformer model, allowing for a more nuanced understanding of real-time preferences. The decision-making process is further refined by generating a group embedding, which is computed by applying a weighted aggregate of the context-infused embeddings of individual group members. This approach models group dynamics and decision processes more accurately, ensuring that the final recommendation reflects the collective preferences of the group. Experiments on real-world Yelp data show that KCGRS significantly outperforms five state-of-the-art baselines, delivering up to an average of 14.15% improvement in Hit ratio and a 13.07% increase in NDCG compared to the next best method while also maintaining competitive runtime efficiency. Furthermore, KCGRS demonstrates enhanced diversity and coverage in recommendations, ensuring that POI suggestions cater to a broader range of user preferences while avoiding over-personalization. This balance between accuracy, diversity, and efficiency highlights KCGRS’s effectiveness in supporting group decision-making and its potential to enhance collaborative recommendations in LBSN platforms. Finally, a user study with 144 participants was conducted that resulted in statistically significant levels of user satisfaction and trust in the recommendations, thereby supporting the practical effectiveness of the KCGRS system.