Personalized Commodity Recommendation Method Based on Behavioral Delay Sharing Network
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摘要: 针对电商平台难以利用历史浏览行为进行个性化商品推荐的问题，该文提出了一种行为延迟共享网络模型(BDSN)，充分结合历史浏览信息，对用户进行精准浏览推荐。该模型提出行为延迟门控循环神经单元(BDGRU)，将历史浏览时间间隔作为用户活跃度因子，对神经元状态进行更新，用于计算用户的兴趣表示。为了提高向量表示的一致性，该模型提出共享参数网络，将用户侧和商品侧的表示向量收敛到统一空间，解决个性化商品推荐点击率预估问题。并在真实数据集上进行实验，结果表明，BDSN模型在验证集上的AUC指标和损失函数均处于最优，在测试集上的AUC指标相较基本模型提高37%，能够有效提升商品推荐的准确性。Abstract: This paper proposes a Behavior Delayed Sharing Network (BDSN) model to solve the personalized product recommendation problem based on personal historical browsing behaviors. First, a Behavior Delay Gated Recurrent Neural Unit (BDGRU) is presented, which uses the historical browsing time interval as a user activity factor, and updates the neuron state to calculate the user's interest expression. Then, a shared parameter network is proposed to converge the representation vectors on the user side and the goods side into a unified space. Experiments show that the AUC index and loss function of BDSN model on the validation set are both optimal, and the AUC index on the test set increases by 37% compared with the basic model.
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表 2 不同方法模型在测试集上的AUC值
模型 AUC RelaImpr(%) BDSN 0.8738 37 DREN 0.8454 27 DNN(BaseModel) 0.7712 0 WideDeep 0.8382 24 PNN 0.8404 25 GRUSN 0.8258 20 BD-Sep 0.8064 12 
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