## 5 Experimental Evaluation
### Experimental Datasets  
To evaluate the effectiveness of APGCN-CF, we conducted comprehensive experiments utilizing two real-world minor agricultural product datasets, with a special focus on scallions and garlic as representative cases. The selection of these two minor agricultural products was based on their distinctive characteristics in agricultural e-commerce: they exhibit significant seasonal harvesting patterns and seasonal demand fluctuations that directly impact supply and pricing, and unlike traditional staple agricultural commodities (such as wheat, rice, and corn) with universal demand patterns, their consumption is heavily influenced by local dietary habits and culinary traditions, displaying pronounced regional characteristics. These minor products present unique recommendation challenges due to their heterogeneous user preference distributions. Both datasets were obtained through web crawling techniques within the publicly accessible scope of the JD shopping platform. The garlic dataset includes 374 users and 816 products, with a total of 3162 interaction records (covering the period from 17 March 2023 to 29 May 2024), with a sparsity of approximately 98.96%. The scallion dataset includes 190 users and 370 products, with a total of 1629 interaction records (covering the period from 17 March 2023 to 29 May 2024), with a sparsity of approximately 97.68%. Each dataset contains four tables: a product table, a user table, a training set, and a test set. The product table records key information about products (examples shown in Table 2), including the following fields: product ID, positive ratings, sales, prices, vectorized store names, vectorized product names, and vectorized comments. Among these, the product ID is the identifying ID of the product, positive ratings indicate the positive review rate of the product, sales indicate the sales volume of the product, and the price is the selling price of the product, while vectorized store names, vectorized product names, and vectorized comments are multi-dimensional vectors converted from textual information using natural language processing models, representing store names, product names, and all historical comments about the product, respectively. This vectorization method preserves semantic information while making it quantifiable. The user table records key information about users (examples shown in Table 3), including the following fields: user ID, vectorized regions, average ratings, and vectorized comments. Among these, the product ID is the identifying ID of the user, the vectorized region is the vectorized identifier of the user's region (embedded uniformly based on geographical proximity relationships), the average rating is the average rating of the user's historical evaluations, and vectorized comments are the vectorized representation of the user's historical reviews (similarly converted from text to numerical vectors using natural language processing models). The training set records interaction information between users and products through reviews (examples shown in Table 4), including the following fields: user ID, product ID, ratings, vectorized comments, and vectorized time.