Recommender systems have become ubiquitous in our digital lives, from
recommending products on e-commerce websites to suggesting movies and music on
streaming platforms. Existing recommendation datasets, such as Amazon Product
Reviews and MovieLens, greatly facilitated the research and development of
recommender systems in their respective domains. While the number of mobile
users and applications (aka apps) has increased exponentially over the past
decade, research in mobile app recommender systems has been significantly
constrained, primarily due to the lack of high-quality benchmark datasets, as
opposed to recommendations for products, movies, and news. To facilitate
research for app recommendation systems, we introduce a large-scale dataset,
called MobileRec. We constructed MobileRec from users' activity on the Google
play store. MobileRec contains 19.3 million user interactions (i.e., user
reviews on apps) with over 10K unique apps across 48 categories. MobileRec
records the sequential activity of a total of 0.7 million distinct users. Each
of these users has interacted with no fewer than five distinct apps, which
stands in contrast to previous datasets on mobile apps that recorded only a
single interaction per user. Furthermore, MobileRec presents users' ratings as
well as sentiments on installed apps, and each app contains rich metadata such
as app name, category, description, and overall rating, among others. We
demonstrate that MobileRec can serve as an excellent testbed for app
recommendation through a comparative study of several state-of-the-art
recommendation approaches. The quantitative results can act as a baseline for
other researchers to compare their results against. The MobileRec dataset is
available at this https URL

MobileRec 是一个大规模的基于 Google Play Store 的数据集，包含了 1900 多万次用户互动和 10K 多种不同类型的应用程序，通过比较研究了几种最先进的推荐方法，MobileRec 可以作为测试应用程序的理想平台，并作为基准线提供量化结果，以供其他研究人员进行比较。