Social Network Content Weighting for Recommendation Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing recommender systems lack accuracy and precision in content recommendations, especially in self-organizing communities and ad-hoc networks where user mobility and feedback variability hinder effective knowledge representation and user profiling.
Innovation Solution
A method that associates content with a weight-value, allowing it to be displayed, updated, and propagated through a social network based on user interactions and connections, enabling more precise and relevant content distribution among users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If collaborative filtering or content-based recommender methods are used, then recommendations can be generated automatically, but accuracy and precision are insufficient compared to real life word-of-mouth
Solution Approach 1:
The system implements feedback mechanisms where users rate or evaluate received content, and this feedback is used to update user profiles and improve future recommendations. The patent specifically mentions obtaining feedback ratings from users and using them to refine the recommendation algorithm, creating a continuous improvement loop that enhances accuracy over time.
Solution Approach 2:
The system performs preliminary actions by pre-calculating user profiles, content characteristics, and similarity metrics before actual recommendation needs arise. User profiles are built in advance based on their interactions, and content is pre-tagged with metadata, enabling faster and more accurate real-time recommendations without compromising precision.
2Adaptability or versatility
If feedback ratings are collected during random connections to other users' ad-hoc devices, then network coverage is improved, but feedback quality becomes useless due to natural differences between persons
Solution Approach 1:
The system applies local quality by creating personalized user profiles that capture individual preferences, behaviors, and characteristics. Instead of treating all users uniformly, the patent implements user-specific weighting factors and preference models that adapt to each user's unique tastes, making feedback from diverse users meaningful and reliable.
Solution Approach 2:
The system dynamically changes parameters such as user weights, trust factors, and preference similarities based on observed user behavior and feedback patterns. The patent mentions adjusting recommendation weights and re-calculating user profiles in real-time, allowing the system to adapt to natural differences between persons while maintaining feedback quality.
3Ease of manufacture
If recommendations are based on product description or meta data, then implementation is simple, but precision and relevance to user preferences are limited
Solution Approach 1:
The system merges multiple recommendation approaches by combining content-based filtering (using product metadata) with collaborative filtering (using user behavior patterns). The patent integrates both methods, using content characteristics as a foundation while layering user profile matching and feedback-based adjustments to achieve high precision without excessive complexity.
Solution Approach 2:
The recommendation system uses composite information structures that combine static content metadata with dynamic user profile data, feedback ratings, and contextual information. This composite approach allows the system to maintain implementation simplicity while achieving precision through the integration of multiple data types and processing layers.
4Device complexity
If remote servers are used for knowledge representation, then centralized processing is achieved, but accessibility is reduced due to client mobility
Solution Approach 1:
The system implements multi-functionality by enabling users to access their profiles and receive recommendations from multiple access points including remote servers, local devices, and peer-to-peer connections. The patent describes a hybrid architecture where user data can be synchronized across different locations and devices, ensuring accessibility regardless of client mobility while maintaining centralized knowledge representation.
Data Source
AI summary
A method of providing content (130) associated with a weight-value, the content (130) previously provided to a current computer associated with a current user (116) that is represented by a first node (106) in a social network (101). The method comprises the steps of: i) enabling the current computer (216) to display the content (130), in dependence of the weight-value, ii) obtaining an input of the current user (116), iii) updating the weight-value of the content (130), in dependence of the input of the current user (116), iv) determining a receiving computer associated with a second node (107) in the social network (101), and v) providing the content (130) to the receiving computer. Corresponding computers, computer program and computer readable medium are also described.