Inferential Newsfeed Generation for Unsubscribed Users
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Solution Overview
Problem
Conventional newsfeed architectures rely on pre-existing user relationships and user-generated content, limiting their ability to provide relevant information to unsubscribed users and requiring users to actively seek out connections and content.
Innovation Solution
A system and method that uses a processor and memory to capture and process user data from various sources, applying rules and execution plans to inferentially generate newsfeeds, identifying commonalities among users, and determining relevant content and subscribers independently of user actions, thereby creating customized newsfeeds for unsubscribed users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional publisher/subscriber architecture is used, then users can receive content from their contacts, but users must actively establish relationships and subscribe to receive content
Solution Approach 1:
The system automatically generates newsfeed content and identifies subscriber relationships without requiring user intervention. The inferential data mining engine autonomously processes user facts, applies rules and execution plans, and determines which users should receive which content based on inferred relationships and commonalities, making the system self-serve rather than user-driven
Solution Approach 2:
The inferential data mining engine acts as an intermediary between raw user data and newsfeed generation. It mines user facts from the data store, applies cached rules and execution plans to infer relationships and commonalities, and automatically determines content delivery targets without direct user input, mediating the entire content distribution process
2Adaptability or versatility
If conventional publisher/subscriber architecture is used, then content is delivered to subscribed users, but unsubscribed users cannot receive relevant content
Solution Approach 1:
Instead of users subscribing to receive content, the system inverts the approach by automatically determining which users should receive which content based on inferred relationships. The inferential data mining engine analyzes user facts and commonalities to proactively deliver relevant content to users who would naturally be interested, regardless of explicit subscriptions
Solution Approach 2:
The system performs preliminary data mining and relationship inference in advance to pre-determine content delivery targets. The cache management module pre-loads and caches rules and execution plans, and the inferential data mining engine pre-processes user facts to identify commonalities and inferred relationships before content generation, enabling proactive content delivery to unsubscribed users
3Quantity of substance
If user-generated content is required, then content authenticity is maintained, but content volume and relevance are limited
Solution Approach 1:
The system generates newsfeed content autonomously by mining user facts from the data store and applying cached rules and execution plans. The inferential data mining engine automatically identifies commonalities among users, generates relevant content, and determines distribution targets without requiring users to create posts, making the content generation process self-serve
Solution Approach 2:
The inferential data mining engine serves as an intermediary that transforms raw user facts into meaningful newsfeed content. It mines structured and unstructured user data, applies inference rules to identify relationships and commonalities, and generates relevant content that would otherwise require manual user creation, bridging the gap between raw data and meaningful content
4Productivity
If manual relationship establishment is required, then relationship authenticity is ensured, but system scalability is limited
Solution Approach 1:
The system automatically infers user relationships by mining user facts and applying rules through the inferential data mining engine. It processes user data elements, generates user facts, and autonomously determines which users have inferred relationships based on commonalities, eliminating the need for manual relationship establishment and enabling automatic scaling as user base grows
Solution Approach 2:
The inferential data mining engine acts as an intermediary that automatically processes user data to infer relationships. It mines user facts from the data store, applies cached execution plans and rules, and determines inferred relationships without manual intervention, serving as an automated mediator that scales with system growth
Data Source
AI summary
A system and method for generating and publishing inferential newsfeeds captures actively and passively provided user data elements which are processed into user facts. A Cache Management Module maintains updated cached copies of rules and execution plans. The cached copies of rules and execution plans are applied to the user facts, to inferentially generate newsfeed posts. An Inferential Data Mining Module runs each execution plan in a multi-threaded process for each user, and processes each rule within the context of the execution plan to generate the newsfeed posts. A Newsfeed Generation Module applies newsfeed templates to format the newsfeeds for various platforms. The Inferential Data Mining Module mines the Data Store for a determination of which user facts should be used to create the newsfeed posts, and of which users should be designated as subscribers to the newsfeed posts, said determination taking place independently of individual users' direct actions.