# Target Apps Selection: Towards a Unified Search Framework for Mobile Devices

arXiv: https://arxiv.org/abs/1805.02211 · HTML: https://mlsanity.com/paper/1805.02211/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2018.

## Built on by (most cited first, 5 of 5)

- [User Response Prediction in Online Advertising](https://mlsanity.com/paper/2101.02342/) (2021)
- [Context-Aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistants](https://mlsanity.com/paper/2101.03394/) (2021)
- [CoSEM: Contextual and Semantic Embedding for App Usage Prediction](https://mlsanity.com/paper/2108.11561/) (2021)
- [Keyword Extraction for Improved Document Retrieval in Conversational Search](https://mlsanity.com/paper/2109.05979/) (2021)
- [TextOnly: A Unified Function Portal for Text-Related Functions on Smartphones](https://mlsanity.com/paper/2508.16926/) (2025)

## Abstract (arXiv)

With the recent growth of conversational systems and intelligent assistants such as Apple Siri and Google Assistant, mobile devices are becoming even more pervasive in our lives. As a consequence, users are getting engaged with the mobile apps and frequently search for an information need in their apps. However, users cannot search within their apps through their intelligent assistants. This requires a unified mobile search framework that identifies the target app(s) for the user's query, submits the query to the app(s), and presents the results to the user. In this paper, we take the first step forward towards developing unified mobile search. In more detail, we introduce and study the task of target apps selection, which has various potential real-world applications. To this aim, we analyze attributes of search queries as well as user behaviors, while searching with different mobile apps. The analyses are done based on thousands of queries that we collected through crowdsourcing. We finally study the performance of state-of-the-art retrieval models for this task and propose two simple yet effective neural models that significantly outperform the baselines. Our neural approaches are based on learning high-dimensional representations for mobile apps. Our analyses and experiments suggest specific future directions in this research area.