Buddy Recommendation via Circle Feature Similarity
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Solution Overview
Problem
Current buddy recommendation mechanisms in social sharing applications are unreliable, resulting in low correlation between recommended buddies and circle members, leading to excessive system resource waste due to many invalid recommendations.
Innovation Solution
A method and apparatus that analyze shared contents of circle members and buddy list members to generate feature information, calculate similarity between them, and recommend buddies based on this similarity, increasing the likelihood of valid recommendations and reducing resource waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If buddy recommendations are made through random selection or simple relationship chain methods, then the recommendation process is simple and fast, but the reliability and relevance of recommendations deteriorate, resulting in many invalid recommendations and system resource waste
Solution Approach 1:
The patent transforms the recommendation approach by changing parameters from simple random selection to multi-dimensional feature analysis. It introduces feature vectors containing user attributes, interest labels, and behavioral data, then calculates similarity based on these parameters. This parameter transformation enables reliable recommendations while maintaining system operability through standardized calculation processes.
Solution Approach 2:
The patent replaces mechanical random selection methods with an information-based similarity calculation system. Instead of using simple algorithms that randomly pick users from relationship chains, it substitutes a computational model that analyzes feature vectors, calculates cosine similarity, and ranks candidates based on relevance scores, thereby improving reliability without excessive complexity.
2Reliability
If comprehensive feature analysis is performed to improve recommendation quality, then recommendation reliability improves, but system resource consumption increases
Solution Approach 1:
The patent applies partial action by selecting only the most relevant features for analysis rather than processing all possible user data. It extracts key attributes such as interest labels, demographic information, and recent behavioral patterns, then calculates similarity based on these selected features. This approach maintains high recommendation reliability while reducing computational overhead and resource consumption compared to analyzing complete user profiles.
Solution Approach 2:
The patent performs preliminary action by pre-processing and storing user feature vectors in advance, organizing data into structured formats with standardized attribute categories. This pre-organization of information allows the recommendation system to quickly retrieve and compare features without performing heavy computation in real-time, thereby reducing instantaneous resource consumption while maintaining analysis comprehensiveness.
3Adaptability or versatility
If more recommended buddies are provided to users, then the comprehensiveness of recommendations improves, but the number of invalid recommendations increases, wasting system resources
Solution Approach 1:
The patent introduces dynamics by implementing a threshold-based filtering mechanism that adapts to different user contexts and circle types. The similarity threshold is not fixed but adjusts based on historical conversion rates, user preferences, and circle characteristics. This dynamic adjustment ensures comprehensive coverage of potential buddies while automatically filtering out low-probability candidates, thereby reducing resource waste on invalid recommendations.
Solution Approach 2:
The patent incorporates feedback mechanisms that use historical recommendation outcomes to refine future recommendation quality. By tracking which recommended users are actually added to circles and analyzing conversion patterns, the system learns from past performance and adjusts similarity thresholds and feature weighting accordingly. This feedback loop maintains comprehensive recommendation coverage while progressively reducing resource waste through improved precision.
Data Source
AI summary
According to various embodiments of the present disclosure, an electronic device analyzes shared contents of all members in a circle created by a client user and generates feature information of the circle. The electronic device analyzes shared contents of each member in a buddy list created by the client user and generates feature information of each member in the buddy list. The electronic device calculates a similarity between the feature information of each member in the buddy list and the feature information of the circle, generates a similarity set, and generates a recommended buddy of the circle based on the similarity set. The electronic device prompts the recommended buddy of the circle to the client user.