SIMILAR APP RECOMMENDATION AND AUTOMATED ASO
Mobile app development is increasingly growing, as there are 3.48M apps present on the play store. So does the increase in the ASO tools for upbringing of an app. So, it is difficult for a developer to implement and search every ASO tool himself and implement that on their app. This is where we
2025-06-28 16:29:04 - Adil Khan
SIMILAR APP RECOMMENDATION AND AUTOMATED ASO
Project Area of Specialization Artificial IntelligenceProject SummaryMobile app development is increasingly growing, as there are 3.48M apps present on the play store. So does the increase in the ASO tools for upbringing of an app. So, it is difficult for a developer to implement and search every ASO tool himself and implement that on their app. This is where we come in and provide our services related to the downloads and app ranking and suggest ASO tools that will help the developer to enhance their ranking in the google app store. This problem can be solved to some extent by developing an automated ASO tool or system that can automatically find the relevant keywords, similar apps and perform competitive analysis.
By using the latest machine learning algorithms, we can train our system to find similar apps, relevant to user. The currently running systems are not performing to its fullest potential and not giving the similar apps as it should be giving. Our aim is to enhance our machinery so that it will provide the most accurate similar apps.
The aim of this project is to help all the android developers who are facing issue regarding the app ranking as it matters to every developer because the ranking of the app is directly related to the downloading of the app. It will also encourage our youth to develop app and earn through the android platform. As it will be good for our country and our community, so our aim is clear in terms of the project and its effects. It will be beneficial not only to the IT guys but also for the marketing people as it is kind of related and have a lot of work regarding marketing.
Project ObjectivesThe product is a web-based application and will be accessed on any device that has an internet connection. AS the increase of applications in the app store in increasing rapidly so does the need of ASO is also increasing day by day. So, in the future the tools will be very helpful for everyone.
App follow is the web app that has the feature of Suggesting ASO tools. It provides you app analytics, competitors, and similar apps.
Project Implementation MethodThe system will be using self-built scrappers for the scrapping of the dataset of the apps from the play store. The scrapper will extract every feature related to the app and provide us with every detail of the app from downloads to rating, ap id to ap icon. The app data will be stored and refreshed from time to time. This collection of app data will be further processed at the backend (for preprocessing) of the data set to make it feasible for us to operate.
For an app developer it is kind of impossible to implement the ASO tools and research on the ASO tools. This is where we come in and provide services to the developers to enhance their App by ASO suggestions. It is kind of frustrating for the developer on the play store to find new keywords for their app from time to time. is quite a difficult task as there are thousands or millions of apps! The developer cannot do it manually because the number of apps and their descriptions are humongous and increasing day by day. If the developer hires someone to check or find relative keywords and similar apps it will take ages and if the person is also hired for the competitive analysis, then it will take ages to complete, then this is costly and time-consuming.
Some web applications do this task like App Follow, app radar. But the Tools provided by these systems are very expensive and not completely relevant sometimes. Our Aim is to improve their flaws and provide a better system with economical rates.
We will be using Agile Development Methodology because agile development allows software to be released in iteration. This helps in improving efficiency with frequent incremental improvements. In iterative life cycle, the development starts by implementing just small part of the system. This process will start when we implement the system requirements; we keep making changes until the system is completely implemented and ready to be deployed.
Benefits of the ProjectMobile app development is increasingly growing, as there are 3.48M apps [1] present on the play store. So does the increase in the ASO tools for upbringing of an app. So, it is difficult for a developer to implement and search every ASO tool himself and implement that on their app. This is where we come in and provide our services related to the downloads and app ranking and suggest ASO tools that will help the developer to enhance their ranking in the google app store. This problem can be solved to some extent by developing an automated ASO tool or system that can automatically find the relevant keywords, similar apps and perform competitive analysis.
By using the latest machine learning algorithms, we can train our system to find similar apps, relevant to user. The currently running systems are not performing to its fullest potential and not giving the similar apps as it should be giving. Our aim is to enhance our machinery so that it will provide the most accurate similar apps.
Technical Details of Final DeliverableMobile app development is increasingly growing, as there are 3.48M apps [1] present on the play store. So does the increase in the ASO tools for upbringing of an app. So, it is difficult for a developer to implement and search every ASO tool himself and implement that on their app. This is where we come in and provide our services related to the downloads and app ranking and suggest ASO tools that will help the developer to enhance their ranking in the google app store. This problem can be solved to some extent by developing an automated ASO tool or system that can automatically find the relevant keywords, similar apps and perform competitive analysis.
By using the latest machine learning algorithms, we can train our system to find similar apps, relevant to user. The currently running systems are not performing to its fullest potential and not giving the similar apps as it should be giving. Our aim is to enhance our machinery so that it will provide the most accurate similar apps.
Mobile app development is increasingly growing, as there are 3.48M apps [1] present on the play store. So does the increase in the ASO tools for upbringing of an app. So, it is difficult for a developer to implement and search every ASO tool himself and implement that on their app. This is where we come in and provide our services related to the downloads and app ranking and suggest ASO tools that will help the developer to enhance their ranking in the google app store. This problem can be solved to some extent by developing an automated ASO tool or system that can automatically find the relevant keywords, similar apps and perform competitive analysis.
By using the latest machine learning algorithms, we can train our system to find similar apps, relevant to user. The currently running systems are not performing to its fullest potential and not giving the similar apps as it should be giving. Our aim is to enhance our machinery so that it will provide the most accurate similar apps.
The project will be completed in july 2022.
Final Deliverable of the Project Software SystemCore Industry ITOther Industries Education Core Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Industry, Innovation and InfrastructureRequired Resources| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 79900 | |||
| GPU | Equipment | 2 | 20000 | 40000 |
| GPU | Equipment | 1 | 30000 | 30000 |
| Thesis printing | Miscellaneous | 3 | 3300 | 9900 |