# MobileRec: A Large-Scale Dataset for Mobile Apps Recommendation

arXiv: https://arxiv.org/abs/2303.06588 · HTML: https://mlsanity.com/paper/2303.06588/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 50 references that are in the map.
- First posted on arXiv 12 Mar 2023.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms](https://mlsanity.com/paper/2011.01731/) (2020)
- [Hierarchical Gating Networks for Sequential Recommendation](https://mlsanity.com/paper/1906.09217/) (2019)
- [Improved Recurrent Neural Networks for Session-based Recommendations](https://mlsanity.com/paper/1606.08117/) (2016)
- [Fine-Grained Spoiler Detection from Large-Scale Review Corpora](https://mlsanity.com/paper/1905.13416/) (2019)
- [Sparse-Interest Network for Sequential Recommendation](https://mlsanity.com/paper/2102.09267/) (2021)
- [On Sampling Top-K Recommendation Evaluation](https://mlsanity.com/paper/2106.10621/) (2021)
- [App-Aware Response Synthesis for User Reviews](https://mlsanity.com/paper/2007.15793/) (2020)
- [Automating App Review Response Generation](https://mlsanity.com/paper/2002.03552/) (2020)
- [ELECRec: Training Sequential Recommenders as Discriminators](https://mlsanity.com/paper/2204.02011/) (2022)

## Built on by (most cited first, 8 of 8)

- [Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation](https://mlsanity.com/paper/2508.18142/) (2025)
- [MobileConvRec: A Conversational Dataset for Mobile Apps Recommendations](https://mlsanity.com/paper/2405.17740/) (2024)
- [A Framework for Generating Conversational Recommendation Datasets from Behavioral Interactions](https://mlsanity.com/paper/2506.17285/) (2025)
- [INTERPOS: Interaction Rhythm Guided Positional Morphing for Mobile App Recommender Systems](https://mlsanity.com/paper/2506.12661/) (2025)
- [LaQual: An Automated Framework for LLM App Quality Evaluation](https://mlsanity.com/paper/2508.18636/) (2025)
- [Security and Privacy Taxonomy Generation from Mobile App Reviews](https://mlsanity.com/paper/2608.09049/) (2026)
- [MBA: Multimodal Benchmark and Agents for Real-World Business Ideation](https://mlsanity.com/paper/2608.11616/) (2026)
- [Challenges and Solutions for Bandits in the Wild: Warm-Started Mixture Bandits for Cross-Cohort Slate Recommendation](https://mlsanity.com/paper/2609.37800/) (2026)

## Abstract (arXiv)

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 https://huggingface.co/datasets/recmeapp/mobilerec.