Bundle generation aims to provide a bundle of items for the user, and has been widely studied and applied on online service platforms. Existing bundle generation methods mainly utilized user's preference from historical interactions in common recommendation paradigm, and ignored the potential textual query which is user's current explicit intention. There can be a scenario in which a user proactively queries a bundle with some natural language description, the system should be able to generate a bundle that exactly matches the user's intention through the user's query and preferences. In this work, we define this user-friendly scenario as Query-based Bundle Generation task and propose a novel framework Text2Bundle that leverages both the user's short-term interests from the query and the user's long-term preferences from the historical interactions. Our framework consists of three modules: (1) a query interest extractor that mines the user's fine-grained interests from the query; (2) a unified state encoder that learns the current bundle context state and the user's preferences based on historical interaction and current query; and (3) a bundle generator that generates personalized and complementary bundles using a reinforcement learning with specifically designed rewards. We conduct extensive experiments on three real-world datasets and demonstrate the effectiveness of our framework compared with several state-of-the-art methods.

翻译：捆绑生成旨在为用户提供一组物品组合，已在在线服务平台得到广泛研究和应用。现有捆绑生成方法主要利用推荐范式中用户历史交互的偏好，忽视了用户当前明确意图的潜在文本查询。存在这样一种场景：用户主动使用自然语言描述查询某个捆绑组合，系统需能够通过用户查询和偏好生成完全匹配其意图的捆绑组合。本文将该用户友好场景定义为基于查询的捆绑生成任务，并提出新型框架Text2Bundle，该框架同时利用查询中的用户短期兴趣和历史交互中的长期偏好。框架包含三个模块：(1) 查询兴趣提取器，从查询中挖掘用户细粒度兴趣；(2) 统一状态编码器，基于历史交互与当前查询学习当前捆绑上下文状态及用户偏好；(3) 捆绑生成器，通过强化学习与专门设计的奖励机制生成个性化且互补的捆绑组合。我们在三个真实数据集上进行大量实验，结果表明相比于多个现有最优方法，本框架具有显著有效性。