# Context-Aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistants

arXiv: https://arxiv.org/abs/2101.03394 · HTML: https://mlsanity.com/paper/2101.03394/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 65 references that are in the map.
- First posted on arXiv 9 Jan 2021.

## Builds on (most cited first, 13 of 13)

- [Asking Clarifying Questions in Open-Domain Information-Seeking Conversations](https://mlsanity.com/paper/1907.06554/) (2019)
- [Neural Ranking Models with Weak Supervision](https://mlsanity.com/paper/1704.08803/) (2017)
- [ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)](https://mlsanity.com/paper/2009.11352/) (2020)
- [Frappe: Understanding the Usage and Perception of Mobile App Recommendations In-The-Wild](https://mlsanity.com/paper/1505.03014/) (2015)
- [Analysing the Effect of Clarifying Questions on Document Ranking in Conversational Search](https://mlsanity.com/paper/2008.03717/) (2020)
- [Harnessing Evolution of Multi-Turn Conversations for Effective Answer Retrieval](https://mlsanity.com/paper/1912.10554/) (2019)
- [Macaw: An Extensible Conversational Information Seeking Platform](https://mlsanity.com/paper/1912.08904/) (2019)
- [Target Apps Selection: Towards a Unified Search Framework for Mobile Devices](https://mlsanity.com/paper/1805.02211/) (2018)
- [Longformer for MS MARCO Document Re-ranking Task](https://mlsanity.com/paper/2009.09392/) (2020)
- [Anticipating Information Needs Based on Check-in Activity](https://mlsanity.com/paper/1709.05749/) (2017)
- [Understanding Mobile Search Task Relevance and User Behaviour in Context](https://mlsanity.com/paper/1812.07081/) (2018)
- [Personalized Ranking for Context-Aware Venue Suggestion](https://mlsanity.com/paper/1705.07311/) (2017)
- [Mobile Information Retrieval](https://mlsanity.com/paper/1902.01790/) (2019)

## Built on by (most cited first, 11 of 11)

- [Predictive Querying for Autoregressive Neural Sequence Models](https://mlsanity.com/paper/2210.06464/) (2022)
- [Expanding the Generative AI Design Space through Structured Prompting and Multimodal Interfaces](https://mlsanity.com/paper/2504.14320/) (2025)
- [MAPLE: Mobile App Prediction Leveraging Large Language Model Embeddings](https://mlsanity.com/paper/2309.08648/) (2023)
- [TGT: A Temporal Gating Transformer for Smartphone App Usage Prediction](https://mlsanity.com/paper/2502.16957/) (2025)
- [Keyword Extraction for Improved Document Retrieval in Conversational Search](https://mlsanity.com/paper/2109.05979/) (2021)
- [Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction](https://mlsanity.com/paper/2303.04744/) (2023)
- [MISApp: Multi-Hop Intent-Aware Session Graph Learning for Next App Prediction](https://mlsanity.com/paper/2603.21653/) (2026)
- [Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates](https://mlsanity.com/paper/2211.06365/) (2022)
- [TextOnly: A Unified Function Portal for Text-Related Functions on Smartphones](https://mlsanity.com/paper/2508.16926/) (2025)
- [ORACLE: Anticipating Scams from Partial Trajectories in Streaming App Usage](https://mlsanity.com/paper/2605.16363/) (2026)
- [STAP: A Shuffle-Tokenized App Predictor with Ultra Long Context for Vocabulary-Free Mobile App Prediction](https://mlsanity.com/paper/2605.29863/) (2026)

## Abstract (arXiv)

Users install many apps on their smartphones, raising issues related to information overload for users and resource management for devices. Moreover, the recent increase in the use of personal assistants has made mobile devices even more pervasive in users' lives. This paper addresses two research problems that are vital for developing effective personal mobile assistants: target apps selection and recommendation. The former is the key component of a unified mobile search system: a system that addresses the users' information needs for all the apps installed on their devices with a unified mode of access. The latter, instead, predicts the next apps that the users would want to launch. Here we focus on context-aware models to leverage the rich contextual information available to mobile devices. We design an in situ study to collect thousands of mobile queries enriched with mobile sensor data (now publicly available for research purposes). With the aid of this dataset, we study the user behavior in the context of these tasks and propose a family of context-aware neural models that take into account the sequential, temporal, and personal behavior of users. We study several state-of-the-art models and show that the proposed models significantly outperform the baselines.