HML4Rec: Hierarchical meta-learning for cold-start recommendation in flash sale e-commerce
Zhi Li, Daichi Amagata, Yihong Zhang, Takuya Maekawa, Takahiro Hara, Kei Yonekawa, Mori Kurokawa
Abstract
Recommender systems (RSs) have been extensively studied in academia and industry, while few works focus on flash sale recommendations. In flash sale scenarios, period-specific high discounts are applied on ordinal sales during each flash sale period to attract users. According to periodic sales strategies, available and discounted items change significantly across periods. Users are attracted by the high discounts and show a period-specific preference. Besides, the frequently changed available items provoke a cold-start problem, i.e., an impaired recommendation caused by lacking interactions. However, most existing RSs either cannot handle users’ period-specific preferences or suffer from the cold-start problem. Therefore, this work proposes a novel meta-learning-based RS to mitigate the cold-start problem and simultaneously handle users’ period-specific preferences. Moreover, we introduce a novel hierarchical meta-training algorithm to guide the learning of our recommendation model via period-and user-specific gradients. In this way, the learned model contains user- and period-shared knowledge and can fast adapt to the recommendations for new flash sale periods and users. To evaluate the effectiveness of our system in flash sale recommendations and non-flash sale recommendations, we conduct experiments on a real-world flash sale e-commerce dataset and a widely used recommendation dataset, considering both warm and cold scenarios. The experimental results show that our proposed model is remarkably improved over the current state-of-the-art methods in flash sale recommendations and most of the non-flash sale cold-start recommendations.