erfanloghmani/myket-android-application-recommendation-dataset

---
license: cc-by-nc-4.0
task_categories:
- graph-ml
size_categories:
- 100K<n<1M
configs:
- config_name: main_data
data_files: "myket.csv"
- config_name: package_name_features
data_files: "app_info.csv"
---
# Myket Android Application Install Dataset

This dataset contains information on application install interactions of users in the [Myket](https://myket.ir/) android application market. The dataset was created for the purpose of evaluating interaction prediction models, requiring user and item identifiers along with timestamps of the interactions.

## Data Creation

The dataset was initially generated by the Myket data team, and later cleaned and subsampled by Erfan Loghmani a master student at Sharif University of Technology at the time. The data team focused on a two-week period and randomly sampled 1/3 of the users with interactions during that period. They then selected install and update interactions for three months before and after the two-week period, resulting in interactions spanning about 6 months and two weeks.

We further subsampled and cleaned the data to focus on application download interactions. We identified the top 8000 most installed applications and selected interactions related to them. We retained users with more than 32 interactions, resulting in 280,391 users. From this group, we randomly selected 10,000 users, and the data was filtered to include only interactions for these users. The detailed procedure can be found in [here](https://github.com/erfanloghmani/myket-android-application-market-dataset/blob/main/create_data.ipynb).

## Data Structure

The dataset has two main files.

- `myket.csv`: This file contains the interaction information and follows the same format as the datasets used in the "[JODIE: Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://github.com/claws-lab/jodie)" (ACM SIGKDD 2019) project. However, this data does not contain state labels and interaction features, resulting in associated columns being all zero.
- `app_info_sample.csv`: This file comprises features associated with applications present in the sample. For each individual application, information such as the approximate number of installs, average rating, count of ratings, and category are included. These features provide insights into the applications present in the dataset. **Note**: The number of installs, average rating, and the count of ratings info are gathered during the period of interaction data collection, so they may cause information leakage if used for interaction prediction tasks.

## Dataset Details

- Total Instances: 694,121 install interaction instances
- Instances Format: Triplets of user_id, app_name, timestamp
- 10,000 users and 7,988 android applications

For a detailed summary of the data's statistics, including information on users, applications, and interactions, please refer to the Python notebook available at [summary-stats.ipynb](https://github.com/erfanloghmani/myket-android-application-market-dataset/blob/main/summary-stats.ipynb). The notebook provides an overview of the dataset's characteristics and can be helpful for understanding the data's structure before using it for research or analysis.

### Top 20 Most Installed Applications

| Package Name | Count of Interactions |
| ---------------------------------- | --------------------- |
| com.instagram.android | 15292 |
| ir.resaneh1.iptv | 12143 |
| com.tencent.ig | 7919 |
| com.ForgeGames.SpecialForcesGroup2 | 7797 |
| ir.nomogame.ClutchGame | 6193 |
| com.dts.freefireth | 6041 |
| com.whatsapp | 5876 |
| com.supercell.clashofclans | 5817 |
| com.mojang.minecraftpe | 5649 |
| com.lenovo.anyshare.gps | 5076 |
| ir.medu.shad | 4673 |
| com.firsttouchgames.dls3 | 4641 |
| com.activision.callofduty.shooter | 4357 |
| com.tencent.iglite | 4126 |
| com.aparat | 3598 |
| com.kiloo.subwaysurf | 3135 |
| com.supercell.clashroyale | 2793 |
| co.palang.QuizOfKings | 2589 |
| com.nazdika.app | 2436 |
| com.digikala | 2413 |

## Comparison with SNAP Datasets

The Myket dataset introduced in this repository exhibits distinct characteristics compared to the real-world datasets used by the project. The table below provides a comparative overview of the key dataset characteristics:

| Dataset | #Users | #Items | #Interactions | Average Interactions per User | Average Unique Items per User |
| --------- | ----------------- | ----------------- | ----------------- | ----------------------------- | ----------------------------- |
| **Myket** | **10,000** | **7,988** | 694,121 | 69.4 | 54.6 |
| LastFM | 980 | 1,000 | 1,293,103 | 1,319.5 | 158.2 |
| Reddit | **10,000** | 984 | 672,447 | 67.2 | 7.9 |
| Wikipedia | 8,227 | 1,000 | 157,474 | 19.1 | 2.2 |
| MOOC | 7,047 | 97 | 411,749 | 58.4 | 25.3 |

The Myket dataset stands out by having an ample number of both users and items, highlighting its relevance for real-world, large-scale applications. Unlike LastFM, Reddit, and Wikipedia datasets, where users exhibit repetitive item interactions, the Myket dataset contains a comparatively lower amount of repetitive interactions. This unique characteristic reflects the diverse nature of user behaviors in the Android application market environment.

## Citation

If you use this dataset in your research, please cite the following [preprint](https://arxiv.org/abs/2308.06862):

```
@misc{loghmani2023effect,
title={Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks},
author={Erfan Loghmani and MohammadAmin Fazli},
year={2023},
eprint={2308.06862},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```

原始信息汇总

Myket Android Application Install Dataset

数据集概述

该数据集包含来自Myket安卓应用市场用户的应用安装交互信息。数据集旨在评估交互预测模型，需要用户和项目标识符以及交互的时间戳。

数据创建

数据集由Myket数据团队初始生成，后由Erfan Loghmani（当时是Sharif University of Technology的硕士学生）进行清理和子采样。数据团队关注两周时间，随机抽样了该期间有交互的1/3用户，并选择了前后三个月的安装和更新交互，导致交互时间跨度约为六个月零两周。

进一步子采样和清理数据，专注于应用下载交互。识别了安装量最高的8000个应用，并选择了与它们相关的交互。保留了超过32次交互的用户，共280,391名用户。从中随机选择了10,000名用户，数据被过滤为仅包含这些用户的交互。详细过程可在此处找到。

数据结构

数据集包含两个主要文件：

• myket.csv: 包含交互信息，格式与"JODIE: Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks"项目中使用的数据集相同，但不包含状态标签和交互特征。
• app_info_sample.csv: 包含样本中应用的相关特征，如安装数量、平均评分、评分数量和类别。

数据集详情

• 总实例数：694,121个安装交互实例
• 实例格式：用户ID、应用名称、时间戳的三元组
• 用户数：10,000，应用数：7,988

详细数据统计信息可在summary-stats.ipynb中找到。

最受欢迎的20个应用

应用包名	交互次数
com.instagram.android	15292
ir.resaneh1.iptv	12143
com.tencent.ig	7919
com.ForgeGames.SpecialForcesGroup2	7797
ir.nomogame.ClutchGame	6193
com.dts.freefireth	6041
com.whatsapp	5876
com.supercell.clashofclans	5817
com.mojang.minecraftpe	5649
com.lenovo.anyshare.gps	5076
ir.medu.shad	4673
com.firsttouchgames.dls3	4641
com.activision.callofduty.shooter	4357
com.tencent.iglite	4126
com.aparat	3598
com.kiloo.subwaysurf	3135
com.supercell.clashroyale	2793
co.palang.QuizOfKings	2589
com.nazdika.app	2436
com.digikala	2413

与其他数据集的比较

数据集	用户数	项目数	交互次数	平均用户交互次数	平均用户独特项目数
Myket	10,000	7,988	694,121	69.4	54.6
LastFM	980	1,000	1,293,103	1,319.5	158.2
Reddit	10,000	984	672,447	67.2	7.9
Wikipedia	8,227	1,000	157,474	19.1	2.2
MOOC	7,047	97	411,749	58.4	25.3

Myket数据集在用户和项目数量上具有优势，适用于大规模实际应用。与LastFM、Reddit和Wikipedia数据集相比，Myket数据集的重复交互较少，反映了安卓应用市场用户行为的多样性。

引用

如在研究中使用此数据集，请引用以下预印本：

@misc{loghmani2023effect, title={Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks}, author={Erfan Loghmani and MohammadAmin Fazli}, year={2023}, eprint={2308.06862}, archivePrefix={arXiv}, primaryClass={cs.LG} }