Key statistics of the experimental dataset.

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.