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Multimodal Data Guided Spatial Feature Fusion and Grouping Strategy for E-Commerce Commodity Demand Forecasting
Mobile Information Systems Pub Date : 2021-06-12 , DOI: 10.1155/2021/5568208
Weiwei Cai 1 , Yaping Song 1 , Zhanguo Wei 1
Affiliation  

E-commerce offers various merchandise for selling and purchasing with frequent transactions and commodity flows. An accurate prediction of customer needs and optimized allocation of goods is required for cost reduction. The existing solutions have significant errors and are unsuitable for addressing warehouse needs and allocation. That is why businesses cannot respond to customer demands promptly, as they need accurate and reliable demand forecasting. Therefore, this paper proposes spatial feature fusion and grouping strategies based on multimodal data and builds a neural network prediction model for e-commodity demand. The designed model extracts order sequence features, consumer emotional features, and facial value features from multimodal data from e-commerce products. Then, a bidirectional long short-term memory network- (BiLSTM-) based grouping strategy is proposed. The proposed strategy fully learns the contextual semantics of time series data while reducing the influence of other features on the group’s local features. The output features of multimodal data are highly spatially correlated, and this paper employs the spatial dimension fusion strategy for feature fusion. This strategy effectively obtains the deep spatial relations among multimodal data by integrating the features of each column in each group across spatial dimensions. Finally, the proposed model’s prediction effect is tested using e-commerce dataset. The experimental results demonstrate the proposed algorithm’s effectiveness and superiority.

中文翻译:

电子商务商品需求预测的多模态数据引导空间特征融合与分组策略

电子商务提供各种商品供销售和采购,交易频繁,商品流动频繁。降低成本需要准确预测客户需求和优化货物分配。现有的解决方案存在重大错误,不适合解决仓库需求和分配。这就是企业无法及时响应客户需求的原因,因为他们需要准确可靠的需求预测。因此,本文提出了基于多模态数据的空间特征融合和分组策略,构建了电子商品需求的神经网络预测模型。设计的模型从电子商务产品的多模态数据中提取订单序列特征、消费者情感特征和面部价值特征。然后,提出了一种基于双向长短期记忆网络(BiLSTM-)的分组策略。所提出的策略充分学习了时间序列数据的上下文语义,同时减少了其他特征对组局部特征的影响。多模态数据的输出特征具有高度空间相关性,本文采用空间维度融合策略进行特征融合。该策略通过跨空间维度整合每组中每列的特征,有效地获得多模态数据之间的深层空间关系。最后,使用电子商务数据集测试了所提出模型的预测效果。实验结果证明了该算法的有效性和优越性。所提出的策略充分学习了时间序列数据的上下文语义,同时减少了其他特征对组局部特征的影响。多模态数据的输出特征具有高度空间相关性,本文采用空间维度融合策略进行特征融合。该策略通过跨空间维度整合每组中每列的特征,有效地获得多模态数据之间的深层空间关系。最后,使用电子商务数据集测试了所提出模型的预测效果。实验结果证明了该算法的有效性和优越性。所提出的策略充分学习了时间序列数据的上下文语义,同时减少了其他特征对组局部特征的影响。多模态数据的输出特征具有高度空间相关性,本文采用空间维度融合策略进行特征融合。该策略通过跨空间维度整合每组中每列的特征,有效地获得多模态数据之间的深层空间关系。最后,使用电子商务数据集测试了所提出模型的预测效果。实验结果证明了该算法的有效性和优越性。本文采用空间维度融合策略进行特征融合。该策略通过跨空间维度整合每组中每列的特征,有效地获得多模态数据之间的深层空间关系。最后,使用电子商务数据集测试了所提出模型的预测效果。实验结果证明了该算法的有效性和优越性。本文采用空间维度融合策略进行特征融合。该策略通过跨空间维度整合每组中每列的特征,有效地获得多模态数据之间的深层空间关系。最后,使用电子商务数据集测试了所提出模型的预测效果。实验结果证明了该算法的有效性和优越性。
更新日期:2021-06-13
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