User Recommendation Engine With Segmented Explanations
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Solution Overview
Problem
Existing social networking platforms lack effective methods to recommend users to follow based on both topical and social linkages, often failing to provide intuitive explanations for these recommendations, which can lead to lower user engagement and loyalty.
Innovation Solution
A system and method that utilize a machine learning-based model to determine user linkages by analyzing topical and social features, recommending users with high linkage scores and providing explanations based on whether the linkage is predominantly topical or social.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system provides detailed recommendations with explanations, then user engagement and loyalty improve, but the system complexity and computational requirements increase
Solution Approach 1:
The recommendation system segments explanations into two distinct types: topical-based explanations (when predominant basis is topical) and social-based explanations (when predominant basis is social). This segmentation allows the system to provide tailored, simplified explanations for different recommendation scenarios, improving user understanding without requiring a single complex explanation framework.
Solution Approach 2:
The patent introduces an intermediary explanation layer that mediates between the complex machine learning model and the user. The explanation module acts as a translator, converting complex model outputs into human-understandable reasons (topical or social basis), thereby maintaining user engagement while shielding users from underlying system complexity.
2Measurement precision
If the system analyzes multiple features (topical and social) to determine user linkages, then recommendation accuracy improves, but the computational processing time and resources increase
Solution Approach 1:
The system determines a subset of users with high linkage scores rather than analyzing all possible user connections. By focusing computational resources on identifying and analyzing only the most relevant candidates (those with high linkage), the system achieves accurate recommendations while reducing overall processing time and computational burden.
Solution Approach 2:
The patent applies different analysis depths and explanation types locally based on the recommendation context. For each recommended user, the system determines whether a topical-based or social-based explanation is more appropriate, analyzing only the relevant feature set for each case rather than uniformly analyzing all features for all recommendations.
3Adaptability or versatility
If the system provides both topical and social feature analysis, then the comprehensiveness of recommendations improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The system dynamically adapts its analysis approach based on the predominant basis determined for each recommendation. Rather than statically analyzing all features uniformly, the system determines whether topical or social features predominate and focuses computational resources accordingly, providing comprehensive coverage when needed while reducing processing for less relevant features.
Solution Approach 2:
The machine learning model serves multiple functions: it simultaneously evaluates topical features, social features, and determines the predominant basis for linkage. This multi-functional approach consolidates what could be separate complex systems into a single unified model, reducing overall device complexity while maintaining comprehensive analysis capabilities.
Data Source
AI summary
Provided herein is a system or method for a users-to-follow recommendation engine for, based at least in part on social network information and information about users in one or more social networks, determining features relating to users, including topical features and social features, determining, using a model constructed utilizing the determined features, for a set or users, a subset of the set of users for which the user has a high linkage, relative to other linkages in the set, and determining, using the model, and displaying to the user, a recommendation to follow and an associated explanation, of at least one particular user of the subset of the users wherein the associated explanation includes a topical-based explanation when a predominant basis for the high linkage is determined to be topical and a social-based explanation when a predominant basis for the high linkage is determined to be social.