An Effective Way for Cross-Market Recommendation with Hybrid Pre-Ranking and Ranking Models

TL;DR

This paper presents a two-stage hybrid ranking approach for cross-market recommendation, leveraging source market data to improve recommendations in resource-scarce target markets, achieving top leaderboard performance.

Contribution

The paper introduces a novel two-stage hybrid pre-ranking and ranking framework specifically designed for cross-market recommendation tasks.

Findings

Achieved first place in WSDM CUP 2022 with NDCG@10 of 0.6773.

Effective feature generation and selection significantly improved recommendation quality.

Hybrid models outperform single-model approaches in cross-market recommendation.

Abstract

The Cross-Market Recommendation task of WSDM CUP 2022 is about finding solutions to improve individual recommendation systems in resource-scarce target markets by leveraging data from similar high-resource source markets. Finally, our team OPDAI won the first place with NDCG@10 score of 0.6773 on the leaderboard. Our solution to this task will be detailed in this paper. To better transform information from source markets to target markets, we adopt two stages of ranking. In pre-ranking stage, we adopt diverse pre-ranking methods or models to do feature generation. After elaborate feature analysis and feature selection, we train LightGBM with 10-fold bagging to do the final ranking.