用轻量置换算法在不降体验前提下提升电商搜索收益
Fast and Feasible: Permutation-based Constrained Reranking for Revenue Maximization
- 基于排列的迭代置换策略，动态调整排序以平衡收益与约束
- 离线实验达ILP收益的63%，线上测试14天提升2%收入
- 适合需实时优化且有严格约束的电商平台推荐系统
搜索与推荐系统已能生成高度相关的结果。在电商场景中，下一步自然是从付费推广角度重新排序以提升平台收入。但单纯追求收益可能降低相关性或增加欺诈风险。为此，我们将重排序问题建模为带每查询约束的整数线性规划（ILP），在最大化收益的同时保证其他指标（如相关性）不越界。由于对每个查询精确求解ILP效率过低，难以用于线上服务，我们提出一种轻量级置换重排序近似算法PermR。该算法在每一步选择一对相邻项目进行交换，以提升目标函数或修复违反的约束。我们在大型分类平台多个品类上进行了离线与在线评估。结果表明，PermR在满足生产延迟要求的前提下，达到约63%的ILP收益提升；在覆盖5600万次搜索请求的14天线上A/B测试中，收入提升了2%。
原文摘要 · Abstract (English)
Search and recommender systems have produced highly relevant search results. A natural next step in the development of such systems in e-commerce is to rerank these results to increase the platform's revenue from paid promotion products. However, maximizing revenue alone may degrade the user experience by reducing relevance or increasing fraud risk. To avoid this, we state the reranking problem as an integer linear program ($ILP$) that maximizes revenue subject to per-query constraints on other metrics, e.g., relevance. Since solving $ILP$ exactly for every query is slow for deployment to the online service, we propose a lightweight permutation-based reranking approximation algorithm PermR. At each step, the algorithm selects a pair of neighboring items and swaps them to either improve the objective or repair a violated constraint. We evaluate PermR across multiple categories of a large classified platform in offline and online settings. PermR achieves about 63\% of the ILP revenue improvement, within production latency limits, preserving all constraints. In a 14-day online A/B test over 56 million search queries, PermR increased revenue by $2$\%.
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