整合搜索与推荐的生成式框架，提升平台转化与点击率
IntSR: An Integrated Generative Framework for Search and Recommendation
- 用不同查询模态统一搜索与推荐任务
- 在高德地图落地后，GMV提升9.34%，点击率增2.76%
- 适合需要融合搜索与推荐场景的工业应用
生成式推荐已成为一个有前景的方向，在学术基准和工业应用中均表现出色。然而，现有系统主要关注检索与排序的统一，忽视了搜索与推荐（S&R）任务的整合。搜索依赖用户显式查询，推荐则基于隐式兴趣；检索与排序的区别在于查询是否为目标物品本身。鉴于查询的核心地位，我们提出IntSR——一种面向搜索与推荐的集成生成框架。IntSR通过不同的查询模态整合两类任务，同时应对集成行为带来的计算复杂度上升以及动态变化语料导致的错误模式学习问题。IntSR已在高德地图多个场景成功部署，显著提升数字资产的GMV（+9.34%）、POI推荐的CTR（+2.76%）及出行方式建议的准确率（+7.04%）。
原文摘要 · Abstract (English)
Generative recommendation has emerged as a promising paradigm, demonstrating remarkable results in both academic benchmarks and industrial applications. However, existing systems predominantly focus on unifying retrieval and ranking while neglecting the integration of search and recommendation (S&R) tasks. What makes search and recommendation different is how queries are formed: search uses explicit user requests, while recommendation relies on implicit user interests. As for retrieval versus ranking, the distinction comes down to whether the queries are the target items themselves. Recognizing the query as central element, we propose IntSR, an integrated generative framework for S&R. IntSR integrates these disparate tasks using distinct query modalities. It also addresses the increased computational complexity associated with integrated S&R behaviors and the erroneous pattern learning introduced by a dynamically changing corpus. IntSR has been successfully deployed across various scenarios in Amap, leading to substantial improvements in digital asset's GMV(+9.34%), POI recommendation's CTR(+2.76%), and travel mode suggestion's ACC(+7.04%).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。