MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation
arxiv(2024)
摘要
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.
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