Probabilistic Filter for Content Selection
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Solution Overview
Problem
Existing content presentation methods are ineffective as they continue to show users products they have already purchased, leading to decreased user engagement and resource wastage, especially after the initial purchase.
Innovation Solution
Implementing a system that uses probabilistic filters to determine if a user has purchased a product by querying a probabilistic database, allowing for the selection of relevant content based on user interaction history, thereby preventing the presentation of related content to users who have already made a purchase.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If content is continuously presented to users without filtering purchase history, then resource utilization is high, but user engagement and content effectiveness decrease
Solution Approach 1:
The patent extracts and filters out content that users have already purchased from the content delivery system. By querying the user's purchase history and excluding already-purchased items from the content pool, the system prevents resource wastage while maintaining content effectiveness. This is achieved through the content delivery management server that intercepts and filters content based on purchase status.
Solution Approach 2:
The system performs preliminary filtering of content based on user purchase history before content is delivered. By pre-querying the purchase history database and identifying already-purchased items in advance, the system avoids delivering irrelevant content, thereby reducing resource wastage and improving content effectiveness simultaneously.
2Measurement precision
If probabilistic filters are used to query purchase history, then content relevance improves, but system complexity increases
Solution Approach 1:
The patent introduces a content delivery management server as an intermediary between the content source and the user device. This intermediary handles the probabilistic filtering and purchase history queries, centralizing the complexity in a dedicated component rather than distributing it throughout the entire system. This approach improves content relevance while managing system complexity through modular architecture.
3Measurement precision
If purchase history is queried for every content request, then content selection accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary queries of the purchase history database to identify already-purchased items before content delivery decisions are made. By pre-fetching and caching relevant purchase information, the system reduces the need for repeated queries during actual content delivery, thereby maintaining high content selection accuracy while reducing processing time.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for filtering data with probabilistic filters for content selection. In one embodiment, an example method may include determining a user interaction history with a first product identifier for a user account, determining a first parent product identifier of the first product identifier, and generating a database with the first parent product identifier and a user account identifier for the user account. Example methods may include determining a set of candidate content with first content and second content for the user account, determining a second product identifier associated with the first content, and determining a second parent product identifier of the second product identifier. Example methods may include determining that the second parent product identifier is not present in the database using a probabilistic filter, and determining that the first content is eligible for presentation.