A Behavioral Data-Based Hybrid Recommendation System to Improve Recommendation Accuracy in Small and Medium Enterprises
Keywords:
Hybrid Recommender System, Behavioral Data, Recommendation Accuracy, Small and Medium Enterprises (SMEs), Collaborative Filtering, Content-Based FilteringAbstract
The rapid growth of digital platforms has increased the need for personalized recommendation systems, especially for small and medium-sized enterprises (SMEs). However, traditional recommendation systems often face significant challenges such as data scarcity and cold-start issues, which hamper their effectiveness in SME environments. This study aims to develop a hybrid recommendation system based on behavioral data to improve recommendation accuracy in SME platforms. A quantitative experimental approach is used by integrating collaborative filtering, content-based filtering, and behavioral interaction modeling into a unified hybrid framework. User behavioral data, including clicks, browsing activity, and purchase history, is processed into a user-item interaction matrix. The proposed model is evaluated using Precision@K, Recall@K, and NDCG@K metrics and compared with several baseline methods.
Experimental results show that the proposed hybrid model outperforms baseline approaches, achieving higher scores in Precision@10, Recall@10, and NDCG@10. Furthermore, the model demonstrates improved performance in handling sparse data and cold-start scenarios, indicating its robustness in real-world SME environments. These findings demonstrate that behavioral data integration significantly improves recommendation accuracy and provides a practical solution for SMEs with limited data interaction. Future research should focus on combining time-sensitive models and deep learning approaches to further improve the performance and scalability of recommender systems.
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