Recommendation systems, which assist users in discovering their preferred items among numerous options, have served billions of users across various online platforms. Intuitively, users' interactions with items are highly driven by their unchanging inherent intents (e.g., always preferring high-quality items) and changing demand intents (e.g., wanting a T-shirt in summer but a down jacket in winter). However, both types of intents are implicitly expressed in recommendation scenario, posing challenges in leveraging them for accurate intent-aware recommendations. Fortunately, in search scenario, often found alongside recommendation on the same online platform, users express their demand intents explicitly through their query words. Intuitively, in both scenarios, a user shares the same inherent intent and the interactions may be influenced by the same demand intent. It is therefore feasible to utilize the interaction data from both scenarios to reinforce the dual intents for joint intent-aware modeling. But the joint modeling should deal with two problems: 1) accurately modeling users' implicit demand intents in recommendation; 2) modeling the relation between the dual intents and the interactive items. To address these problems, we propose a novel model named Unified Dual-Intents Translation for joint modeling of Search and Recommendation (UDITSR). To accurately simulate users' demand intents in recommendation, we utilize real queries from search data as supervision information to guide its generation. To explicitly model the relation among the triplet <inherent intent, demand intent, interactive item>, we propose a dual-intent translation propagation mechanism to learn the triplet in the same semantic space via embedding translations. Extensive experiments demonstrate that UDITSR outperforms SOTA baselines both in search and recommendation tasks.

翻译：推荐系统通过在海量选项中帮助用户发现其偏好物品，已在各类在线平台服务数十亿用户。直观而言，用户与物品的交互行为主要受两种意图驱动：恒定的固有意图（例如始终偏好高质量物品）和变化的需求意图（例如夏季需要T恤而冬季需要羽绒服）。然而在推荐场景中，这两类意图均以隐式方式表达，这为利用其实现精准的意图感知推荐带来了挑战。值得关注的是，在同一在线平台中常与推荐服务并存的搜索场景里，用户通过查询词显式表达了其需求意图。从机理上看，在这两种场景中用户共享相同的固有意图，且其交互行为可能受到相同需求意图的影响。因此，利用双场景的交互数据来增强双意图表征，进而实现联合意图感知建模具有可行性。但联合建模需解决两个核心问题：1）在推荐场景中精准建模用户的隐式需求意图；2）建模双意图与交互物品间的关联关系。为解决这些问题，本文提出名为统一双意图翻译搜索推荐联合模型（UDITSR）的新型框架。为精确模拟推荐场景中的用户需求意图，我们利用搜索数据中的真实查询作为监督信息来引导需求意图生成。为显式建模<固有意图，需求意图，交互物品>三元组的关系，我们提出双意图翻译传播机制，通过嵌入翻译在统一语义空间中学习该三元组表示。大量实验表明，UDITSR在搜索与推荐任务中均优于现有最优基线模型。