A Topic Recommendation Control Method Based on Topic Relevancy and R-tree Index
Jing Yu, Zhixing Lu, Xianghua Li, Bin Yan Wu, Shunli Zhang, Zongmin Cui · International Journal of Computers Communications & Control · 2024
Topic recommendation control aims to suggest relevant topics to users based on their preferences and regional trends. However, existing methods often lack effective measures to evaluate topic-user relevancy and require comparing large amounts of regional information, leading to low accuracy and efficiency. Therefore, we propose a Topic Recommendation Control method based on topic Relevancy and R-tree index (named as TRCRR) to address these limitations. TRCRR introduces a novel personalized topic relevancy metric that quantifies the relevancy between topics and user preferences. To improve efficiency, an R-tree topic index is constructed to organize topics across different regions hierarchically. Experiments on a real-world dataset show that TRCRR achieves better recommendation accuracy and efficiency compared to several baseline methods. The proposed approach offers a promising solution for personalized and region-aware topic recommendation.