Modern feed recommendation and search systems are deeply connected in user behavior butare usually modeled by separate architectures. Feed recommendation mainly captures implicitinterests from browsing interactions, while search systems rely on explicit user queries to retrieveintent-matched content. This separation causes fragmented user understanding and missedopportunities for using feed interactions to improve query generation and using generated queriesto enhance feed candidate retrieval.In this paper, we propose OneFeed, a unified generative framework for jointly modelingfeed content enhancement and query generation. OneFeed encodes heterogeneous user behaviorsequences with a shared behavior encoder and employs two generative heads: a Feed SemanticID Generator that produces content semantic IDs for recommendation retrieval, and an IntentQuery Generator that produces natural-language queries for search-based candidate expansion.To bridge the semantic gap between recommendation content and search queries, we introduce aSID-Query alignment objective that learns a shared semantic space for content semantic IDs andquery representations. We further design a closed-loop self-enhancement paradigm that leveragesimplicit user feedback from generated content and search-retrieved results to improve bothgeneration tasks. We provide a detailed experimental protocol using public recommendationdatasets with weakly supervised query construction, define a comprehensive set of evaluationmetrics, report expected performance estimates grounded in known baseline values, and validatethe executability of the proposed pipeline through a minimal local prototype. OneFeed providesa practical and extensible direction for unifying search and recommendation through generativemodeling.

翻译：现代Feed推荐系统与搜索系统在用户行为上紧密关联，但通常采用独立架构进行建模。Feed推荐主要通过浏览交互捕捉隐式兴趣，而搜索系统依赖显式用户查询检索意图匹配内容。这种分离导致用户理解碎片化，并错失了利用Feed交互改进查询生成以及通过生成查询增强Feed候选检索的机遇。 本文提出OneFeed——一种用于联合建模Feed内容增强与查询生成的统一生成式框架。OneFeed通过共享行为编码器编码异构用户行为序列，并采用两个生成头：用于推荐检索的Feed语义ID生成器（生成内容语义ID）和用于搜索候选扩展的意图查询生成器（生成自然语言查询）。为弥合推荐内容与搜索查询之间的语义鸿沟，我们引入SID-Query对齐目标，学习内容语义ID与查询表示的共享语义空间。我们进一步设计闭环自增强范式，利用生成内容与搜索检索结果中的隐式用户反馈改进两个生成任务。我们基于公开推荐数据集采用弱监督查询构建方法提供详细实验方案，定义全面评估指标，基于已知基线值报告预期性能估计，并通过最小化本地原型验证所提管线的可执行性。OneFeed为通过生成式建模统一搜索与推荐提供了实用且可扩展的方向。