论文标题
跨市场推荐的实用的两阶段排名框架
A Practical Two-stage Ranking Framework for Cross-market Recommendation
论文作者
论文摘要
跨市场建议旨在通过利用类似富裕资源市场的用户行为来推荐资源 - 分类目标市场中的用户，这对电子商务公司至关重要，但受到研究的关注较少。 In this paper, we present our detailed solution adopted in the cross-market recommendation contest, i.e., WSDM CUP 2022. To better utilize collaborative signals and similarities between target and source markets, we carefully consider multiple features as well as stacking learning models consisting of deep graph recommendation models (Graph Neural Network, DeepWalk, etc.) and traditional recommendation models (ItemCF, UserCF, Swing, etc.).此外，我们采用基于树的结合方法，例如LightGBM，在预测任务中显示出卓越的性能以生成最终结果。我们在XMREC数据集上进行了全面的实验，以验证模型的有效性。我们团队WSDM_COGGLE_的建议解决方案被选为第二名提交。
Cross-market recommendation aims to recommend products to users in a resource-scarce target market by leveraging user behaviors from similar rich-resource markets, which is crucial for E-commerce companies but receives less research attention. In this paper, we present our detailed solution adopted in the cross-market recommendation contest, i.e., WSDM CUP 2022. To better utilize collaborative signals and similarities between target and source markets, we carefully consider multiple features as well as stacking learning models consisting of deep graph recommendation models (Graph Neural Network, DeepWalk, etc.) and traditional recommendation models (ItemCF, UserCF, Swing, etc.). Furthermore, We adopt tree-based ensembling methods, e.g., LightGBM, which show superior performance in prediction task to generate final results. We conduct comprehensive experiments on the XMRec dataset, verifying the effectiveness of our model. The proposed solution of our team WSDM_Coggle_ is selected as the second place submission.
