To balance low latency and high accuracy in cross-lingual real-time recommendation, we propose a two-stage method combining offline high-precision entity representation with online low-latency feature interaction. First, the cross-language scenario is abstracted as a heterogeneous graph. Then, a Siamese Graph Convolutional Network is utilized for entity representation learning. Finally, an efficient bilinear attention mechanism is employed for deep feature interaction to output predictions. After conducting experiments on the cross-border e-commerce dataset, it was found that the model performed well in entity representation learning. When the recommendation list length was 30, the normalized discounted cumulative gain value of the Siamese graph convolutional network was stable at more than 7.8%, which was more than 20% higher than other models. Regarding feature interaction, the bilinear attention mechanism showed superior convergence. Its mean average value reached 12.7% in the 100th round, 1.9 percentage points higher than the bilinear mechanism. In the scenario of increasing the sales rate of “long-tail products,” the hit rate of the recommendation method proposed by the study reached 46.5%. In summary, the proposed method demonstrates excellent accuracy and efficiency, proving its potential for real-time cross-language applications.
Ting Hu (Thu,) studied this question.