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 (MSNet) 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：相比企业-消费者（B2C）电子商务系统，消费者-消费者（C2C）电子商务平台通常面临限量库存问题，即一件商品在C2C系统中仅能销售一次。这为点击率（CTR）预测带来了若干独特挑战。由于每件商品（即物品）的用户交互有限，CTR模型中对应的物品嵌入可能难以收敛。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，因为历史用户行为包含库存量不同的混合商品。特别地，序列模型中的注意力机制倾向于为累积用户交互更多的商品分配更高分数，导致限量库存商品被忽视且对最终输出的贡献更小。为此，我们提出Meta-Split网络（MSNet）以根据每件商品的库存量对用户历史序列进行分拆，并对不同序列采用差异化的建模方法。针对限量库存商品，采用元学习方法解决不收敛问题，具体通过设计结合ID和辅助信息的元缩放与移位网络实现。此外，传统方法在商品售罄后难以更新物品嵌入。因此，我们提出一种辅助损失函数，使参数即使在商品脱离分布后仍可更新。据我们所知，这是首个针对限量库存商品推荐的解决方案。在生产数据集上的实验结果和在线A/B测试验证了所提方法的有效性。