A fundamental technique of recommender systems involves modeling user preferences, where queries and items are widely used as symbolic representations of user interests. Queries delineate user needs at an abstract level, providing a high-level description, whereas items operate on a more specific and concrete level, representing the granular facets of user preference. While practical, both query and item recommendations encounter the challenge of sparse user feedback. To this end, we propose a novel approach named Multiple-round Auto Guess-and-Update System (MAGUS) that capitalizes on the synergies between both types, allowing us to leverage both query and item information to form user interests. This integrated system introduces a recursive framework that could be applied to any recommendation method to exploit queries and items in historical interactions and to provide recommendations for both queries and items in each interaction round. Empirical results from testing 12 different recommendation methods demonstrate that integrating queries into item recommendations via MAGUS significantly enhances the efficiency, with which users can identify their preferred items during multiple-round interactions.

翻译：推荐系统的一项基础技术涉及用户偏好建模，其中查询与项目被广泛用作用户兴趣的符号化表征。查询在抽象层面勾勒用户需求，提供高层级描述；而项目则在更具体、更精细的层面运作，表征用户偏好的细粒度维度。尽管实用，查询推荐与项目推荐均面临用户反馈稀疏的挑战。为此，我们提出一种名为多轮自动推测更新系统（MAGUS）的新方法，该方法充分利用两类表征间的协同效应，使我们能够综合查询与项目信息以构建用户兴趣画像。该集成系统引入了一种递归框架，可应用于任意推荐方法，以挖掘历史交互中的查询与项目信息，并在每轮交互中同时提供查询与项目推荐。通过对12种不同推荐方法的实证测试表明，借助MAGUS将查询信息整合至项目推荐中，能显著提升用户在多轮交互过程中定位心仪项目的效率。