  A unified mobile search framework aims to identify the mobile apps that can
satisfy a user's information need and route the user's query to them. Previous
work has shown that resource descriptions for mobile apps are sparse as they
rely on the app's previous queries. This problem puts certain apps in dominance
and leaves out the resource-scarce apps from the top ranks. In this case, we
need a ranker that goes beyond simple lexical matching. Therefore, our goal is
to study the extent of a BERT-based ranker's ability to improve the quality and
diversity of app selection. To this end, we compare the results of the
BERT-based ranker with other information retrieval models, focusing on the
analysis of selected apps diversification. Our analysis shows that the
BERT-based ranker selects more diverse apps while improving the quality of
baseline results by selecting the relevant apps such as Facebook and Contacts
for more personal queries and decreasing the bias towards the dominant
resources such as the Google Search app.
