Individual and group behavior-based customer profile model for personalized product recommendation
The development of efficient customer profile models is crucial for improving the recommendation quality of the recommendation system. In this paper, we propose a new customer profile model based on individual and group behavior information such as clicks, basket insertions, purchases, and interest fields. We also implement a recommendation system using the proposed model, and evaluate the recommendation performance of the proposed model in terms of several well known evaluation metrics. Experimental results show that the proposed model has a better recommendation performance than existing models.
customer profile recommendation system collaborative filtering content-based filtering