erfanloghmani/myket-android-application-recommendation-dataset

Myket Android Application Install Dataset

This dataset contains information on application install interactions of users in the Myket 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.

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" (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.

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. 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:

@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}
}