MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation
<|reference_start|>MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation: 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.<|reference_end|>
@article{wu2024metasplit:,
    title={MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation},
    author={Wenhao Wu, Jialiang Zhou, Ailong He, Shuguang Han, Jufeng Chen, Bo
  Zheng},
    journal={arXiv preprint arXiv:2403.06747},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06747},
    primaryClass={cs.IR}
}