BERT for Target Apps Selection: Analyzing the Diversity and Performance of BERT in Unified Mobile Search

TL;DR

This paper evaluates a BERT-based ranking model for unified mobile search, demonstrating its ability to enhance app diversity and relevance over traditional models by better capturing query intent.

Contribution

It provides an analysis of BERT's effectiveness in improving app diversity and relevance in mobile search, addressing resource sparsity issues in app descriptions.

Findings

BERT-based ranker increases app diversity in search results.

BERT improves relevance for personal queries like Facebook and Contacts.

BERT reduces bias towards dominant apps like Google Search.

Abstract

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.