User Data Aging Parameters for Scalable Content Recommendations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing content recommendation systems face challenges in managing large volumes of user data efficiently, particularly in real-time scenarios with millions of users, leading to delays and inaccurate recommendations due to uneven aging of user actions and storage constraints.
Innovation Solution
A method and system that adjusts the frequency of aging out learn actions based on variable parameters such as storage capacity, user actions, and content recommendation requests, ensuring an optimal number of learn actions are maintained for accurate and efficient content recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a fixed time period is used for aging out learn actions, then storage capacity is managed, but users with high interaction frequencies experience delays in receiving recommendations
Solution Approach 1:
The patent implements dynamic aging out by adjusting the time period for retaining learn actions based on user interaction frequency. Users with higher interaction frequencies have their learn actions retained for longer periods, while users with lower frequencies have theirs aged out more quickly. This dynamic adjustment resolves the contradiction by adapting the storage management policy to individual user behaviors, maintaining storage efficiency while ensuring timely recommendations for active users.
Solution Approach 2:
The system changes the time period parameter for aging out learn actions based on user-specific interaction frequencies. Instead of a uniform fixed time period, the parameter is adjusted according to each user's behavior patterns, allowing the system to optimize between storage capacity management and recommendation speed for different user segments.
2Measurement precision
If learn actions are retained longer to improve recommendation accuracy, then user profile accuracy improves, but storage requirements increase
Solution Approach 1:
The patent applies parameter changes by adjusting the retention time period for learn actions based on user interaction frequencies. This allows the system to maintain accurate user profiles for active users who generate valuable learn actions, while reducing storage requirements for less active users whose learn actions would be retained indefinitely under a fixed long-term policy.
Solution Approach 2:
The system applies different retention policies to different users based on their local characteristics (interaction frequencies). Rather than a uniform policy, each user's learn actions are managed according to their specific behavior patterns, allowing the system to optimize storage usage while maintaining profile accuracy where it matters most.
3Loss of time
If user data is processed in real-time to meet response constraints, then recommendation timeliness improves, but system complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-calculating and storing user interaction frequencies, which are then used to determine aging out parameters. This preparation work is done in advance, allowing the system to make quick aging out decisions without complex real-time calculations, thus reducing response time while managing system complexity through pre-computation.
Solution Approach 2:
The system uses user-generated interaction data to automatically determine their own aging out parameters without requiring external intervention or complex centralized control. Each user's behavior patterns self-determine the retention policy applied to their data, simplifying the overall system architecture while enabling timely recommendations.
Data Source
AI summary
A method of managing storage of user data of a content recommendation system is provided. The content recommendation system comprises a user learning module for receiving data indicative of user actions, determining one or more learn actions based on the received data and storing the learn actions on a storage resource. The method comprises determining a parameter associated with aging out a learn action stored on a storage resource of a content recommendation system. The learn action determined based on data indicative of a user action. The method further comprises aging out the learn action stored on the storage resource based on the parameter.