Existing recommendation systems have focused on two paradigms: 1- historical
user-item interaction-based recommendations and 2- conversational
recommendations. Conversational recommendation systems facilitate natural
language dialogues between users and the system, allowing the system to solicit
users' explicit needs while enabling users to inquire about recommendations and
provide feedback. Due to substantial advancements in natural language
processing, conversational recommendation systems have gained prominence.
Existing conversational recommendation datasets have greatly facilitated
research in their respective domains. Despite the exponential growth in mobile
users and apps in recent years, research in conversational mobile app
recommender systems has faced substantial constraints. This limitation can
primarily be attributed to the lack of high-quality benchmark datasets
specifically tailored for mobile apps. To facilitate research for
conversational mobile app recommendations, we introduce MobileConvRec.
MobileConvRec simulates conversations by leveraging real user interactions with
mobile apps on the Google Play store, originally captured in large-scale mobile
app recommendation dataset MobileRec. The proposed conversational
recommendation dataset synergizes sequential user-item interactions, which
reflect implicit user preferences, with comprehensive multi-turn conversations
to effectively grasp explicit user needs. MobileConvRec consists of over 12K
multi-turn recommendation-related conversations spanning 45 app categories.
Moreover, MobileConvRec presents rich metadata for each app such as permissions
data, security and privacy-related information, and binary executables of apps,
among others. We demonstrate that MobileConvRec can serve as an excellent
testbed for conversational mobile app recommendation through a comparative
study of several pre-trained large language models.

现有的推荐系统主要集中在两个范式上：一是基于历史用户 - 项目交互的推荐，二是会话式推荐。对话式推荐系统促进用户和系统之间的自然语言对话，允许系统获取用户明确需求并允许用户提问和提供反馈。鉴于自然语言处理的重大进展，对话式推荐系统日益受到关注。现有的对话式推荐数据集在各自领域的研究中发挥了重要作用。然而，近年来移动用户和应用程序呈指数增长，对话式移动应用推荐系统的研究面临重大限制，主要归因于缺乏专门针对移动应用程序的高质量基准数据集。为促进对话式移动应用推荐的研究，我们引入了 MobileConvRec。MobileConvRec 通过利用用户在 Google Play 商店上与移动应用的真实交互进行对话模拟，该交互数据最初捕获在大规模移动应用推荐数据集 MobileRec 中。MobileConvRec 将连续的用户 - 项目交互（反映隐式用户偏好）与全面的多轮对话相结合，以有效把握用户的明确需求。MobileConvRec 包括超过 12,000 个涵盖 45 个应用类别的多轮推荐相关对话。此外，MobileConvRec 为每个应用程序提供丰富的元数据，如权限数据，安全和隐私相关信息以及应用程序的二进制可执行文件等。通过对几个预训练的大型语言模型的比较研究，我们证明了 MobileConvRec 可作为对话式移动应用推荐的优秀测试平台。