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Recommendation system for online gaming platform
Blue Ocean Gaming engaged one of our Senior Data Scientists, to boost user engagement through personalized game recommendations. Using advanced algorithms, they developed a two-phase system: fast retrieval and ranking. This dynamic approach significantly improved user satisfaction and game relevance.
Services
Data Science
Project Length
3 Months
Client
Blue Ocean Gaming
Our Planning Process
Online gaming platforms with extensive game libraries face the challenge of helping users discover games aligned with their preferences. Personalized recommendations are crucial for enhancing user engagement, retention, and platform competitiveness. Investing in advanced recommendation algorithms and machine learning is essential for providing tailored gaming experiences and staying competitive in the dynamic gaming industry. To accomplish this, we leveraged advanced recommendation algorithms and machine learning techniques. Our approach involved storing input data within a relational database, specifically MySQL, from which we constructed a feature store to build the recommendation system using Python.
What we did for Blue Ocean Gaming
Our recommendation system consists of two main phases designed to provide users with the best gaming experiences.
Phase 1: Fast Retrieval System
We developed a fast retrieval system that continuously updates itself with the latest data. This system rapidly identifies the most suitable games for each user based on the user behavior in the past. It retrieves trending games, finds games similar to those recently played by the user, and explores other relevant recommendations. Additionally, this stage is employed to make the pool of games much smaller and relevant because the initial pool is very big. This ensures that users receive up-to-date and personalized game suggestions.
Phase 2: Ranking System
Once the fast retrieval system identifies potential game choices, the second stage employs a ranking algorithm. This algorithm assigns scores to each of the retrieved games based on their compatibility with the user's preferences. It considers factors such as gameplay history, user behavior, and other metrics to determine the most appealing games. This second algorithm/model is trained on a more comprehensive dataset, incorporating data about the user, the game, the relationship between the user and the game, game type, and more. The ranked list is then presented to the user, ensuring that the top recommendations are the most likely to match their gaming preferences.
Together, these two stages create a dynamic and responsive recommendation system that not only adapts to the user's changing tastes but also ensures that the most relevant and enjoyable games are consistently highlighted.
Our recommendation system consists of two main phases designed to provide users with the best gaming experiences.
Phase 1: Fast Retrieval System
We developed a fast retrieval system that continuously updates itself with the latest data. This system rapidly identifies the most suitable games for each user based on the user behavior in the past. It retrieves trending games, finds games similar to those recently played by the user, and explores other relevant recommendations. Additionally, this stage is employed to make the pool of games much smaller and relevant because the initial pool is very big. This ensures that users receive up-to-date and personalized game suggestions.
Phase 2: Ranking System
Once the fast retrieval system identifies potential game choices, the second stage employs a ranking algorithm. This algorithm assigns scores to each of the retrieved games based on their compatibility with the user's preferences. It considers factors such as gameplay history, user behavior, and other metrics to determine the most appealing games. This second algorithm/model is trained on a more comprehensive dataset, incorporating data about the user, the game, the relationship between the user and the game, game type, and more. The ranked list is then presented to the user, ensuring that the top recommendations are the most likely to match their gaming preferences.
Together, these two stages create a dynamic and responsive recommendation system that not only adapts to the user's changing tastes but also ensures that the most relevant and enjoyable games are consistently highlighted.
Final Results
The implementation of our comprehensive recommendation system has achieved exceptionally positive results, significantly enhancing the gaming experience for the users.
User Engagement and Satisfaction: Our recommendation system has effectively provided users with up-to-date and personalized game suggestions while simultaneously reducing the pool of game choices to a more manageable and relevant selection. This has resulted in increased user engagement and higher levels of user satisfaction.
Enhanced Game Relevance: The recommendation system, powered by extensive user and game data, has consistently delivered game recommendations closely aligned with each user's preferences. Users are now more likely to discover and enjoy games that match their gaming style and interests.