Compared to business-to-consumer (B2C) e-commerce systems, consumer-to-consumer (C2C) e-commerce platforms usually encounter the limited-stock problem, that is, a product can only be sold one time in a C2C system. This poses several unique challenges for click-through rate (CTR) prediction. Due to limited user interactions for each product (i.e. item), the corresponding item embedding in the CTR model may not easily converge. This makes the conventional sequence modeling based approaches cannot effectively utilize user history information since historical user behaviors contain a mixture of items with different volume of stocks. Particularly, the attention mechanism in a sequence model tends to assign higher score to products with more accumulated user interactions, making limited-stock products being ignored and contribute less to the final output. To this end, we propose the Meta-Split Network (MSN) to split user history sequence regarding to the volume of stock for each product, and adopt differentiated modeling approaches for different sequences. As for the limited-stock products, a meta-learning approach is applied to address the problem of inconvergence, which is achieved by designing meta scaling and shifting networks with ID and side information. In addition, traditional approach can hardly update item embedding once the product is consumed. Thereby, we propose an auxiliary loss that makes the parameters updatable even when the product is no longer in distribution. To the best of our knowledge, this is the first solution addressing the recommendation of limited-stock product. Experimental results on the production dataset and online A/B testing demonstrate the effectiveness of our proposed method.

翻译：摘要：与商业对消费者（B2C）电商系统相比，消费者对消费者（C2C）电商平台通常面临有限库存问题，即商品在C2C系统中只能被售卖一次。这为点击率（CTR）预测带来了若干独特挑战。由于每个商品（即物品）的用户交互有限，CTR模型中对应的物品嵌入难以收敛。这使得基于传统序列建模的方法无法有效利用用户历史信息，因为历史用户行为中混合了不同库存量的商品。特别地，序列模型中的注意力机制倾向于将更高分数分配给累计交互更多的商品，导致有限库存商品被忽略，对最终输出贡献更少。为此，我们提出元分割网络（MSN），根据每种商品的库存量对用户历史序列进行分割，并对不同序列采用差异化的建模方法。针对有限库存商品，采用元学习方法解决收敛问题，通过设计基于ID和侧信息的元缩放与偏移网络实现。此外，传统方法在商品被消耗后难以更新物品嵌入。因此，我们提出一种辅助损失函数，使得即使商品不再处于分布中，参数仍可更新。据我们所知，这是首个针对有限库存商品推荐的解决方案。在生产数据集和在线A/B测试上的实验结果证明了我们提出方法的有效性。