Social Graph Recommendation Engine for Targeted Information Delivery
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Solution Overview
Problem
Current recommendation systems generate information that is not sufficiently targeted to individual users, leading to inefficiencies in finding relevant information due to reliance on statistical methods rather than personalized social relationships and behavior records.
Innovation Solution
An information recommendation method that acquires a user's friend list and behavior records from social relationships and user behavior databases to generate targeted recommendation information, improving user experience by matching current user behavior with friend behavior patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If statistical method is used to generate recommendation information, then the system is simple to implement, but the recommendation information is not targeted to each individual user
Solution Approach 1:
The patent introduces a social relationship graph as an intermediary layer between users and recommendation information. Instead of directly analyzing user behavior statistics, the system uses friends' behavior records as mediators to infer user preferences, thereby achieving more targeted recommendations while maintaining implementation simplicity through the structured social graph framework
Solution Approach 2:
The patent copies behavior records from friends (proxy users) to generate recommendations for the target user. By analyzing what friends have done and using this copied information as a proxy for the user's own preferences, the system achieves personalized recommendations without requiring direct analysis of the user's complete behavior history
2Productivity
If recommendation information is generated based on user behavior records only, then the system is fast to compute, but the recommendation lacks social context and personalization
Solution Approach 1:
The patent merges multiple data sources including user behavior records, friends' behavior records, and social relationship information into a unified recommendation generation process. This combination allows the system to maintain computational efficiency by processing data in parallel while significantly improving recommendation relevance through the integrated social context
3Manufacturing precision
If the system acquires and processes friend behavior records, then the recommendation becomes more targeted, but the system complexity increases
Solution Approach 1:
The patent segments the recommendation system into distinct functional modules: friend list acquisition unit, friend behavior record acquisition unit, and recommendation information generation unit. Each module handles a specific aspect of the complex task independently, making the overall system more manageable and easier to implement despite the increased functionality
Solution Approach 2:
The patent designs a universal recommendation engine that can handle multiple types of data (user behavior, friend behavior, social relationships) through a single integrated architecture. This multi-functional design reduces the need for separate specialized systems, thereby managing complexity while providing comprehensive targeted recommendations
Data Source
AI summary
An information recommendation method, a recommendation engine, and a network system are disclosed in embodiments of the present invention. The method includes: acquiring a friend list of a user from a data source with a social relationship; acquiring a behavior record of a friend in the friend list of the user from a user behavior database; generating recommendation information matched with current behavior of the user, according to the behavior record of the friend in the friend list of the user and information of the current behavior of the user; and sending the recommendation information to an application website. Through the embodiments of the present invention, when information is recommended to a user, the recommendation information can be generated based on the social relationship and according to the behavior record of the friend of the user.