  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.
