Personalized Information Recommendation via Social and Behavioral Data Aggregation
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Solution Overview
Problem
Current information recommendation systems on the Internet often provide monotonous and inaccurate content, failing to consider individual user characteristics such as age, gender, interests, and social interactions, resulting in limited and irrelevant recommendations.
Innovation Solution
An information processing method that obtains user-specific data, including basic user information, user behavior, and relationship chain information, to provide personalized and diverse recommendations by aggregating media information associated with user interactions and social interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional information recommendation is performed using Internet media, then information can be recommended to users, but the recommended information content becomes monotonous and limited
Solution Approach 1:
The patent combines multiple information sources including real-time hot search keywords, user behavior data, and social relationship chain information to create comprehensive recommendation sets. This merging of diverse data sources resolves the contradiction by maintaining information variety while enabling personalized recommendations.
Solution Approach 2:
The patent introduces a social relationship chain dimension to traditional recommendation systems. By incorporating user social connections and their interaction data, the system adds a new dimension of information variety, resolving the monotony problem while maintaining recommendation capability.
2Measurement precision
If traditional information recommendation is performed without considering user individualities, then information can be recommended, but the recommendation accuracy decreases
Solution Approach 1:
The patent segments user data into distinct categories including basic user information, user behavior information, and user relationship chain information. This segmentation allows the system to process complex data systematically, improving recommendation accuracy while managing data processing complexity through structured approaches.
Solution Approach 2:
The patent performs preliminary processing of user data by pre-collecting and organizing user profiles, behavior patterns, and social relationship information before recommendation generation. This preliminary action reduces real-time processing complexity while maintaining high recommendation accuracy.
3Measurement precision
If personalized information recommendation is implemented considering user individualities, then recommendation accuracy improves, but the system complexity increases
Solution Approach 1:
The patent creates a universal data processing framework that handles multiple types of user data (basic information, behavior data, relationship information) through standardized processes. This multi-functional approach improves recommendation accuracy while controlling system complexity by reusing processing components across different data types.
Solution Approach 2:
The patent introduces intermediary processing layers that transform complex raw user data into structured features suitable for recommendation algorithms. These intermediary representations simplify the mapping between diverse user data and recommendation outputs, improving accuracy while reducing system complexity.
Data Source
AI summary
The present disclosure provides an information processing method, a server, a terminal, and a computer storage medium. The method includes: obtaining first information from a terminal, the first information comprising at least a media object on which an operation is performed and a user identifier; obtaining second information associated with the media object on which the operation is performed, the second information comprising more than one piece of media information; obtaining third information according to the user identifier, the third information comprising at least basic user information, user behavior information, and user relationship chain information; obtaining fourth information associated with the third information, the fourth information comprising more than one piece of media information; and sending the second information and the fourth information to the terminal, so that the terminal aggregates and displays the media information in the second information and the fourth information.