Historical Interaction Weighting for Faster Object List Access
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Solution Overview
Problem
Providing access to a set of objects associated with an entity is inefficient due to unoptimized arrangement, especially when the set includes numerous individual objects.
Innovation Solution
A system determines weights for each object based on historical interactions with the entity, using a weighted algorithm or trained machine-learning model, to generate sorted lists that optimize the presentation of objects in a customizable user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If objects are provided in a default or unoptimized arrangement, then the system structure remains simple, but the efficiency of object access deteriorates
Solution Approach 1:
The system performs preliminary sorting of objects based on historical interaction data before the user needs to access them. By pre-calculating and pre-arranging objects in the optimal order using machine learning models, the system eliminates the need for complex real-time sorting operations, thus improving access efficiency without proportionally increasing system complexity
Solution Approach 2:
The system automatically sorts and arranges objects based on its own analysis of historical data and usage patterns, without requiring manual user configuration or intervention. The machine learning model self-adjusts the sorting criteria based on observed entity behavior, making the system adaptive and efficient while maintaining relatively simple operational interfaces
2Measurement precision
If objects are sorted based on comprehensive historical data analysis, then object presentation accuracy improves, but data processing time increases
Solution Approach 1:
The system performs comprehensive data analysis and model training in advance, storing the learned patterns and weights for quick retrieval. By pre-processing historical data and establishing the sorting model beforehand, the system achieves high prediction accuracy without incurring the full data processing time during actual object access operations
Solution Approach 2:
The system uses a weighted approach where not all historical data points are equally processed during sorting. Instead, it applies weights to different features and historical interactions based on their relevance, processing only the most significant factors during runtime. This partial processing approach maintains high accuracy while reducing the effective data processing time during object presentation
Data Source
AI summary
A system can receive a request to access objects of an entity. The system can access historical data relating to the entity. The system can determine weights corresponding to the objects. The system can determine weights corresponding to the objects based on a number of times a corresponding object has been used by the entity over a predetermined time period, a most recent use of the corresponding object, and an interaction type for which the corresponding object has been most used over the predetermined time period. The system can generate a sorted list by applying the weights. The system can provide a user interface to the user device that presents a first object in a first location and a second object in a second location that is different than the first location based on respective weights of the first object and the second object.