Social Network Game Recommendation Incentives
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Solution Overview
Problem
Online games often fail to engage users, resulting in wasted development efforts due to uninteresting game names or logos, leading to low playtime and costs for game developers.
Innovation Solution
A system and method that uses social network data analysis to recommend online games to friends, providing incentives for both the recommender and the target friend, based on user profiles and gaming behavior, to increase game engagement and retention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If game names or logos are used to attract users, then user interest may be generated, but users still do not play the game resulting in wasted development efforts
Solution Approach 1:
The system implements feedback loops where user gaming behavior data is continuously collected, analyzed, and used to generate personalized game recommendations. This feedback mechanism ensures that development efforts are directed toward games that users actually engage with, rather than relying on superficial attraction methods like names or logos.
Solution Approach 2:
The system changes the parameter of user selection from superficial attributes (game names, logos) to data-driven parameters (user behavior patterns, preferences, social network data). This parameter change enables more accurate matching of users to games they will actually play, reducing wasted development efforts.
2Productivity
If social network data analysis is implemented to generate game recommendations, then user engagement increases, but system complexity increases
Solution Approach 1:
The system performs multiple functions using a unified approach: it collects social network data, analyzes gaming behavior patterns, generates recommendations, and provides incentives all through an integrated platform. This multi-functionality reduces the need for separate complex systems for each task.
Solution Approach 2:
The system automatically analyzes user data and generates recommendations without requiring manual intervention. The incentive generation is also automated based on predefined rules and user actions, reducing operational complexity while maintaining high engagement levels.
3Productivity
If incentives are provided to users for recommending games, then game sharing and promotion increase, but costs for game developers increase
Solution Approach 1:
The system provides incentives selectively rather than universally - only to users who actually refer friends who play games. This partial action approach ensures that developer costs are incurred only when there is actual value generated (new players), rather than providing blanket incentives that would increase costs without proportional benefits.
Data Source
AI summary
Systems and methods for processing recommendations of online games to friends of social network are described. A method for processing recommendations includes identifying a gaming session of a user on an online game provider network, accessing a use profile of the user for the online games of the online game provider network, and accessing a social graph of the user to identify friends of the user and respective friend profiles from the social network. The method further includes producing a recommendation of an online game. The recommendation includes an identification of a target friend of the user and is being provided by examination of the use profile of the user and friend profiles in the social graph. The method includes providing the recommendation to the user. The recommendation also includes an offer incentive to the user to share the recommendation with the target friend.