Recommender systems and search engines serve as foundational elements of online platforms, with the former delivering information proactively and the latter enabling users to seek information actively. Unifying both tasks in a shared model is promising since it can enhance user modeling and item understanding. Previous approaches mainly follow a discriminative paradigm, utilizing shared encoders to process input features and task-specific heads to perform each task. However, this paradigm encounters two key challenges: gradient conflict and manual design complexity. From the information theory perspective, these challenges potentially both stem from the same issue -- low mutual information between the input features and task-specific outputs during the optimization process. To tackle these issues, we propose GenSR, a novel generative paradigm for unifying search and recommendation (S&R), which leverages task-specific prompts to partition the model's parameter space into subspaces, thereby enhancing mutual information. To construct effective subspaces for each task, GenSR first prepares informative representations for each subspace and then optimizes both subspaces in one unified model. Specifically, GenSR consists of two main modules: (1) Dual Representation Learning, which independently models collaborative and semantic historical information to derive expressive item representations; and (2) S&R Task Unifying, which utilizes contrastive learning together with instruction tuning to generate task-specific outputs effectively. Extensive experiments on two public datasets show GenSR outperforms state-of-the-art methods across S&R tasks. Our work introduces a new generative paradigm compared with previous discriminative methods and establishes its superiority from the mutual information perspective.

翻译：推荐系统与搜索引擎作为在线平台的基础组件，前者主动推送信息，后者支持用户主动检索。在统一模型中整合这两类任务具有重要前景，因其能够增强用户建模与物品理解能力。现有方法主要遵循判别式范式，利用共享编码器处理输入特征，并通过任务特定头部执行各自任务。然而该范式面临两大关键挑战：梯度冲突与人工设计复杂性。从信息论视角分析，这些挑战可能源于同一本质问题——优化过程中输入特征与任务特定输出间的互信息不足。为解决这些问题，我们提出GenSR，一种统一搜索与推荐（S&R）任务的新型生成式范式，通过任务特定提示将模型参数空间划分为子空间，从而提升互信息水平。为构建各任务的有效子空间，GenSR首先为每个子空间准备信息丰富的表征，进而在统一模型中同步优化所有子空间。具体而言，GenSR包含两大核心模块：（1）双视图表征学习模块，通过独立建模协同历史信息与语义历史信息，获得具有强表达力的物品表征；（2）S&R任务统一模块，结合对比学习与指令微调技术，高效生成任务特定输出。在两个公开数据集上的大量实验表明，GenSR在S&R任务上全面超越现有最优方法。相较于传统判别式方法，本研究从互信息视角出发，提出了一种全新的生成式范式并验证了其优越性。