Multidimensional Data Structures for Fantasy Sports Content Delivery
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Solution Overview
Problem
Content management systems face challenges in efficiently managing computing resources to deliver contextually relevant content to remote devices, particularly on mobile devices with limited resources, where irrelevant content can degrade performance and user experience.
Innovation Solution
The system generates a multidimensional data structure based on user profiles with similar attributes, such as player lineups and contest participation, to optimize content delivery. This involves maintaining user profiles, identifying player lineups and contests, generating a multidimensional data structure, and selecting content based on associations between player and contest attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content management systems deliver content to a large number of remote computing devices, then content coverage and user reach are improved, but computing resources are consumed excessively and device performance deteriorates
Solution Approach 1:
The system performs preliminary actions by generating multidimensional data structures and identifying content associations in advance, before actual content delivery. This allows the system to pre-process and organize content based on user profiles and attributes, reducing the computational burden during actual content transmission and display operations on remote devices.
Solution Approach 2:
The system applies local quality by delivering contextually relevant content tailored to specific user profiles and device characteristics. Instead of delivering uniform content to all devices, the system customizes content delivery based on individual user attributes, player lineups, and contest participations, ensuring that each device receives only the content appropriate for its user, thereby reducing overall computing resource consumption.
2Adaptability or versatility
If contextually irrelevant content is delivered to mobile devices, then content variety is improved, but device performance and user experience deteriorate
Solution Approach 1:
The system implements feedback mechanisms by continuously analyzing user interactions, contest participations, and profile attributes to refine content delivery. The multidimensional data structures capture user behavior patterns and preferences, allowing the system to learn from user responses and adjust content selection accordingly, ensuring that content variety is maintained while consistently delivering contextually relevant material that enhances user experience.
Solution Approach 2:
The system applies parameter changes by dynamically adjusting content delivery parameters based on user profiles, contest types, and engagement patterns. The multidimensional data structures enable the system to modify content selection criteria, delivery timing, and presentation formats according to changing user preferences and contest contexts, thereby maintaining content variety while ensuring relevance and improving ease of operation.
3Ease of operation
If content is optimized for each individual user, then user experience is improved, but system complexity increases
Solution Approach 1:
The system merges multiple user profiles and their associated attributes into unified multidimensional data structures. By combining information from numerous user profiles, contest participations, and player lineups into integrated data models, the system achieves personalized content delivery without proportionally increasing complexity. The merging approach allows shared patterns and commonalities to be exploited, reducing the overall computational burden compared to completely independent personalization for each user.
Solution Approach 2:
The multidimensional data structures serve multiple functions simultaneously: they store user profile information, track contest participations, analyze player lineups, and guide content selection. This multi-functionality reduces system complexity by using a single unified data structure rather than separate specialized structures for each function, thereby achieving personalized content delivery without proportionally increasing system complexity.
Data Source
AI summary
Systems and methods for generating a multidimensional data structure based on fantasy sports account activity are described herein. Processors can maintain user profiles, each user profile having player lineups associated with fantasy sports contests. The processors can identify, for a first user profile, player lineups of the first user profile and respective contests for which the player lineups were entered. Each player lineup including players having players attributes. Each contest having contest attributes. The processors can generate, for the first user profile, a multidimensional data structure including a plurality of features. Each feature can have a respective value that is based on the player attributes corresponding to the players included in the player lineups and the contest attributes corresponding to the contests for which the player lineups were entered. The processors can then provide content selected using the generated multidimensional data structure to a device associated with the user profile.