Social Graph Aggregation for Targeted Product Recommendations
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Solution Overview
Problem
Current information retrieval systems fail to provide users with easy and quick access to specific, targeted, and relevant recommendations for products, especially in online shopping, which is challenging for both consumers and online stores, and lacks a user-friendly, developer-friendly, and financially effective solution.
Innovation Solution
The system leverages a social graph of a social networking site to obtain and share size data and user interest information of minors, allowing authorized users to receive targeted product recommendations from ecommerce partners, thereby facilitating personalized shopping experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing ad, search and recommendation technologies are used to provide targeted product recommendations, then users can receive some level of product information, but the process requires substantial time and resources that are not easily captured into the system
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing size data, user interest information, and social graph data in advance. When a user initiates a shopping session, the system already has this information ready to quickly generate targeted recommendations without requiring real-time data gathering, thus reducing information gathering time while maintaining targeting accuracy.
Solution Approach 2:
The patent introduces an intermediary aggregation engine that collects data from multiple sources (social graphs, ecommerce partners, user profiles) and processes it into ready-to-use recommendations. This intermediary layer handles the complex data processing in advance, allowing the final recommendation system to quickly deliver targeted results without users experiencing the time cost of data collection and processing.
2Measurement precision
If comprehensive user data is collected to provide personalized recommendations, then recommendation accuracy improves, but system complexity and resource requirements increase
Solution Approach 1:
The system segments the complex data collection and processing task into distinct modules: social graph data collection, size data collection, user interest information gathering, and recommendation generation. Each module handles a specific aspect of data processing independently, which reduces overall system complexity while enabling comprehensive data collection for accurate personalized recommendations.
Solution Approach 2:
The aggregation engine serves multiple functions: it collects data from various sources, processes and stores the information, generates recommendations, and integrates with different ecommerce partners. This multi-functional design consolidates what would otherwise require multiple separate systems, reducing overall system complexity while maintaining comprehensive data processing capabilities for accurate recommendations.
3Productivity
If traditional shopping recommendation systems are used, then general product information can be provided, but they fail to provide easy and quick access to specific, targeted and relevant recommendations
Solution Approach 1:
The system performs preliminary data collection on user profiles, social connections, and size information before shopping sessions begin. This pre-processing allows the system to quickly retrieve highly relevant recommendations during actual shopping, achieving both high information retrieval speed and high recommendation relevance simultaneously.
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions with recommendations, shopping behavior, and size data updates continuously refine the recommendation algorithm. This feedback loop enables the system to learn from user preferences and improve both the speed and relevance of recommendations over time, addressing the contradiction between quick retrieval and precise targeting.
Data Source
AI summary
A system configured to leverage a social graph of a social networking site may be provided. A social graph of a social networking site may be interfaced with to obtain size data from a guardian's account for at least one minor, the minor's size data being one or more letters or numbers representative of a series of measurements for manufactured article. The minor's size data may be provided to one or more users who are authorized by the guardian account to access the minor's size data. The minor's size data may be provided to one or more ecommerce partners. Offers, from the one or more ecommerce partners, may be generated directed to the one or more authorized users, for commercially available manufactured articles that match the minor's size data. When a guardian posts data about the minor on the social network using the inventive system, the posted data is automatically packaged as a scrapbook and sent to authorized users outside of the social network.