## 2 Related Work
### Content-Based Filtering (CBF) and Collaborative Filtering (CF)  
Content-based filtering (CBF) approaches recommend items to users based on the similarity between items they have previously shown preference for. These methods primarily analyze item similarity and user preferences through item-specific content, including detailed descriptions and manually extracted feature sets. While CBF implementations utilize various machine learning algorithms, such as k-nearest neighbors [6], naive Bayes classifiers, and decision rule-based classification systems, they exhibit limitations in the context of minor agricultural product recommendations. Specifically, CBF methodologies do not fully leverage user rating data, which constitute an essential information source for comprehending both user preferences and intrinsic item characteristics; however, these existing studies on predicting user preferences through users' previous behaviors have provided fundamental insights for this paper, namely, analyzing user preferences by integrating historical interaction records to recommend products that users might appreciate. Collaborative filtering (CF) approaches tackle the recommendation challenge by analyzing user-item interaction patterns, predominantly through user-provided item ratings. Among these methods, matrix factorization (MF) [7] stands as a prominent implementation of latent factor models, which characterizes both users and items through factor vectors derived from rating patterns. Subsequent research has introduced several advanced variations of this approach, including probabilistic matrix factorization (PMF) and singular value decomposition (SVD) [6; 7]. [PERSON] and [PERSON] [8] identify a fundamental \"latency problem\" inherent to collaborative filtering methods, which \"occurs when new items are frequently inserted into the database\" and \"is characterized by the system's failure to recommend new items\". This limitation is particularly relevant for agricultural products with strong seasonal characteristics, where timely recommendations of newly available items are essential. CF techniques exhibit limitations as they primarily rely on historical rating data without incorporating item content features. [PERSON] et al. [9] highlight a critical challenge in CF implementation: while theoretically straightforward, CF requires a user-item evaluation matrix that is often extremely sparse, with general users purchasing only approximately 1% of available items in typical e-commerce systems. This sparsity problem is particularly pronounced for minor agricultural products such as scallions and ginger, which exhibit strong seasonality and regional specificity and are heavily influenced by local dietary customs and traditions. For these niche agricultural products with a limited consumer base, determining the nearest neighbor user set becomes exceptionally difficult in sparse data environments, and the computational cost of similarity calculations increases significantly. Consequently, CF techniques demonstrate suboptimal performance in scenarios involving these highly personalized and dynamically changing items with distinct regional and seasonal characteristics. To overcome the inherent limitations of individual recommendation approaches, hybrid collaborative filtering (HCF) methodologies integrate multiple recommendation techniques to enhance system performance. Specifically, certain hybrid frameworks initially employ user-based collaborative filtering to identify users with similar preference patterns, followed by item-based collaborative filtering to generate recommendations based on these users' historical interaction data [10; 11].