Time-Series Candidate Ranking for Personalized Content
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Solution Overview
Problem
Existing content recommendation systems face issues with inaccurate and computationally expensive rankings due to the unavailability of streaming time data, especially when dealing with multiple applications, and the inefficiency of machine learning models trained on streaming time.
Innovation Solution
A machine learning model trained using a reinforcement learning algorithm optimizes content candidate rankings based on time series data, including dwell time, user clicks, and business targets, to provide improved personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained on streaming time data for content ranking, then ranking accuracy is improved, but computational cost and training time increase significantly
Solution Approach 1:
The patent pre-calculates and stores engagement metrics (play counts, completion rates, user interactions) for all content candidates before ranking is needed. This preliminary data preparation allows the machine learning model to perform rankings quickly without extensive real-time computation, resolving the contradiction between accuracy and training time by doing the heavy lifting in advance.
Solution Approach 2:
The patent creates simplified proxy metrics that capture the essence of user engagement without requiring full streaming time data. These proxy metrics (play counts, completion rates) serve as substitutes for the computationally expensive streaming time measurements, maintaining ranking accuracy while reducing computational burden and training time.
2Measurement precision
If streaming time data is used for ranking content candidates, then ranking accuracy is improved, but the system becomes more complex due to data availability issues across multiple applications
Solution Approach 1:
The patent implements a universal engagement tracking system that works across multiple applications and content types. Instead of requiring application-specific streaming time data, the system uses standardized engagement metrics (play counts, completion rates, user interactions) that can be collected from any application, reducing system complexity while maintaining ranking accuracy.
Solution Approach 2:
The patent introduces an intermediary layer that aggregates engagement data from multiple applications into a unified format. This intermediary tracking system translates diverse application-specific data into standardized metrics, simplifying the overall system architecture by eliminating the need for each application to implement its own complex tracking mechanism.
3Productivity
If traditional ranking methods are used without time series data, then computational efficiency is maintained, but ranking accuracy and personalization quality deteriorate
Solution Approach 1:
The patent extracts only the most relevant engagement features from time series data (recent play counts, completion rates, trending metrics) rather than using the complete historical data stream. This selective extraction maintains computational efficiency by reducing the data volume processed while improving ranking accuracy by focusing on the most predictive features.
Solution Approach 2:
The patent uses a partial approach to time series analysis by considering only recent engagement windows and key metrics rather than the complete historical record. This partial action maintains computational efficiency while capturing sufficient signal for accurate personalization, avoiding the excessive computational cost of analyzing all available historical data.
Data Source
AI summary
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for candidate ranking for content recommendation. An embodiment operates by identifying category candidates and time series data associated with the category candidates, wherein each of the category candidates corresponds to a respective theme comprising a plurality of content candidates associated with the theme. The embodiment ranks the category candidates based on a machine model trained using a learning algorithm based on the time series data, and ranks the content candidates in the each of category candidates based on the time series data. The embodiment then causes the ranked category candidates and the ranked content candidates to be outputted for display.