Relation-aware Meta-learning for E-commerce Market Segment Demand Prediction with Limited Records
| Autor: | Xian Wu, Huaxiu Yao, Tengfei Wang, Tong Li, Jiatu Shi, Zedong Lin, Binqiang Zhao |
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| Rok vydání: | 2021 |
| Předmět: |
Meta learning (computer science)
Relation (database) Process (engineering) Computer science business.industry Initialization Context (language use) 02 engineering and technology E-commerce 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Market segmentation 020204 information systems 0202 electrical engineering electronic engineering information engineering Artificial intelligence Representation (mathematics) business computer 0105 earth and related environmental sciences |
| Zdroj: | WSDM |
| DOI: | 10.1145/3437963.3441750 |
| Popis: | E-commerce business is revolutionizing our shopping experiences by providing convenient and straightforward services. One of the most fundamental problems is how to balance the demand and supply in market segments to build an efficient platform. While conventional machine learning models have achieved great success on data-sufficient segments, it may fail in a large-portion of segments in E-commerce platforms, where there are not sufficient records to learn well-trained models. In this paper, we tackle this problem in the context of market segment demand prediction. The goal is to facilitate the learning process in the target segments by leveraging the learned knowledge from data-sufficient source segments. Specifically, we propose a novel algorithm, RMLDP, to incorporate a multi-pattern fusion network (MPFN) with a meta-learning paradigm. The multi-pattern fusion network considers both local and seasonal temporal patterns for segment demand prediction. In the meta-learning paradigm, transferable knowledge is regarded as the model parameter initialization of MPFN, which are learned from diverse source segments. Furthermore, we capture the segment relations by combining data-driven segment representation and segment knowledge graph representation and tailor the segment-specific relations to customize transferable model parameter initialization. Thus, even with limited data, the target segment can quickly find the most relevant transferred knowledge and adapt to the optimal parameters. We conduct extensive experiments on two large-scale industrial datasets. The results justify that our RMLDP outperforms a set of state-of-the-art baselines. Besides, RMLDP has been deployed in Taobao, a real-world E-commerce platform. The online A/B testing results further demonstrate the practicality of RMLDP. |
| Databáze: | OpenAIRE |
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