Machine Learning Recommendation Networking for Purchase Advice
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Solution Overview
Problem
Users face challenges in identifying knowledgeable friends or family members for purchasing decisions due to lack of awareness of their recent purchases, and online reviews are often unreliable and overwhelming.
Innovation Solution
A machine learning model analyzes purchase intention, social networking, and purchase history data to recommend connections between users, prompting knowledgeable individuals to share insights on potential purchases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users rely on online reviews for purchasing decisions, then they can gather information from multiple sources, but the volume and reliability of reviews becomes overwhelming and unreliable
Solution Approach 1:
The patent introduces social network connections as an intermediary between users and product information. Instead of directly consuming online reviews, users are connected to friends and family members who have personal experiences with products, serving as trusted intermediaries who filter and validate information reliability.
Solution Approach 2:
The patent replaces the mechanical system of manually reading and analyzing numerous online reviews with an automated machine learning system that identifies and connects users with relevant social network members, substituting manual information gathering with automated intelligent matching.
2Reliability
If users ask friends and family for advice, then they receive more reliable and valuable input, but users cannot identify which connections have relevant purchase experience
Solution Approach 1:
The patent replaces manual assessment of connection relevance with an automated machine learning model that analyzes social network data, purchase history, and product information to automatically identify which friends or family members have relevant purchase experience, eliminating the difficulty of manual detection.
Solution Approach 2:
The patent transforms the way relevance is measured by changing from manual evaluation to automated feature-based analysis. The machine learning model processes multiple parameters including social network connections, purchase history, product characteristics, and temporal data to dynamically determine relevance.
3Loss of information
If users conduct extensive online research, then they gather more information, but the time required for purchasing decisions increases
Solution Approach 1:
The patent performs preliminary action by pre-identifying and connecting users with relevant social network members before the purchasing decision is made. The system proactively surfaces trusted advisors who can provide immediate, targeted advice, eliminating the need for users to conduct extensive independent research.
Solution Approach 2:
The patent uses social network connections as intermediaries to deliver curated, pre-processed information directly to users. Instead of users searching through vast online information, trusted intermediaries filter and present only relevant advice, significantly reducing decision time while maintaining information completeness.
Data Source
AI summary
Methods, systems, and apparatuses are described herein for providing purchase recommendations by analyzing social networks using machine learning. A machine learning model may be trained to select one or more of the first plurality of users. Purchase intention data that indicates an intention of a first user to acquire a type of asset may be received. Social networking data that comprises a plurality of associations between a second plurality of users may be received. Purchase history data indicating one or more purchases, of one or more assets associated with the type of asset, made by the second plurality of users may be received. The trained machine learning model may be provided the data. In return, the trained machine learning model may provide an indication of a second user. A notification may be sent to the second user.