The paper focuses on investigating the Collaborative Filtering Recommendation (CFR) algorithm within the recommendation system and the recommendation model of an agricultural product mall. Taking into account the influence of scoring time characteristics, a novel CF algorithm is proposed by combining these characteristics. The CF algorithm model is implemented through the analysis of user data and commodity data. By considering the user’s historical purchase information and online behavior records in conjunction with time characteristics, the proposed CF algorithm based on time characteristics aims to enhance the accuracy and efficiency of recommendations. The experimental results show that the proposed model can improve the recommendation quality and efficiency of the agricultural products mall.
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