<![CDATA[Complementary product recommendation plays an important role in E-commerce platforms by assisting users in discovering products that are likely to be purchased together. Many existing approaches treat this task primarily as a predictive modeling problem and directly apply machine learning models to transactional data. However, such approaches often overlook the underlying information structure of product relationships. This study formulates complementary recommendation as a relational information modeling problem in which transactional purchase data are transformed into structured product interactions. Products are first represented symbolically and mapped into numeric indices through vocabulary-based encoding functions. The encoded identifiers are then transformed into vector-space representations that capture semantic proximity between products and categories. Complementary prediction is performed by applying an interaction inference function to structured product-pair representations derived from these vectors. Experimental results using real-world E-commerce transaction data demonstrate stable learning convergence with a Mean Absolute Error (MAE) of 0.0106 and a Mean Squared Error (MSE) of 0.0035, indicating that the model effectively captures complementary relationships. From an informatics perspective, this work highlights how recommendation tasks can be formulated as structured information transformation processes rather than solely predictive modeling tasks to support cross-selling and product bundling strategies. ]]>
