  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.
