Abstract
Aiming to discover competitive new products, sales forecasting has been playing an increasingly important role in real-world E-Commerce systems. Current methods either only utilize historical sales records with time series based models, or train powerful classifiers (e.g., DNN and GBDT) with subtle feature engineering. Despite effectiveness, they have limited abilities to make prediction for new products due to the sparsity of product-related features. With the observation on real-world data, we find that some additional time series features (e.g., brand and category) implying product characteristics also play vital roles in new product sales forecasting. Hence, we organize them as a new kind of dense feature called CPV (Category-Property-Value) and propose a Time Series aware Heterogeneous Graph (TSHG) to integrate CPVs and products based time series into a unified framework for fine-grained interaction. Furthermore, we propose a novel Graph Attention Networks based new product Sales Forecasting model (GASF) that jointly exploits high-order structure and time series features derived from THSG for new product sales forecasting with graph attention networks. Moreover, a multi trend attention (MTA) mechanism is also proposed to solve temporal shifting and spatial inconsistency between the time series of products and CPVs. Extensive experiments on an industrial dataset and online system demonstrate the effectiveness of our proposed approaches.
C. Xu and X. Wang—Contribute equally to this work.
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Xu, C. et al. (2021). Graph Attention Networks for New Product Sales Forecasting in E-Commerce. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12683. Springer, Cham. https://doi.org/10.1007/978-3-030-73200-4_39
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