Model and training hyperparameter configuration.

The recommendation algorithm suggests products to users, improving their experience, however, it encounters a challenge of insufficient diversity in the recommended results. This paper proposes Product Path and Time decay enhanced Product-based Neural Network recommendation algorithm. Firstly, establishes three types of product paths: User Purchase History Path, Product Similarity Calculation Path, and Product Bundles Path, integrates them to form a comprehensive product relation network, thereby enhancing the diversity of the recommended results. Then, a time decay function is introduced to further improve recommendation accuracy of the recommended products. Finally, fuses the product path and time decay function as a new R component to the Product layer of the PNN model. Experimental results show that the Product Path and Time decay enhanced PNN model improves the AUC from 0.8605 to 0.8772 and reduces the cross-entropy loss from 0.2228 to 0.2155. Meanwhile, the intra-list diversity (ILD) increases from 0.8581 to 0.8832, and the entropy rises from 4.15 to 4.74, demonstrating superiority over the standard PNN model in both accuracy and recommendation diversity.

推荐算法通过向用户推送商品以优化其使用体验，但面临推荐结果多样性不足的挑战。本文提出一种融合商品路径与时间衰减机制的基于商品的神经网络（Product-based Neural Network, PNN）推荐算法。首先构建三类商品路径：用户购买历史路径、商品相似度计算路径与商品捆绑路径，将其整合为综合商品关系网络，以此提升推荐结果的多样性；随后引入时间衰减函数以进一步优化推荐商品的精准度；最后将商品路径与时间衰减机制融合，作为新增的R分量嵌入PNN模型的商品层。实验结果表明，经商品路径与时间衰减增强的PNN模型，其AUC从0.8605提升至0.8772，交叉熵损失从0.2228降至0.2155；同时，列表内多样性（intra-list diversity, ILD）从0.8581提升至0.8832，信息熵从4.15升至4.74，在推荐精准度与多样性两方面均优于标准PNN模型。