Purchase Metric Computation for Device Recommendation Accuracy
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Solution Overview
Problem
Existing systems for recommending electronic devices to users lack accuracy in suggesting devices based on user preferences and purchase patterns, as they do not effectively utilize purchase metrics such as purchase delay and preferred discounts.
Innovation Solution
A media guidance application that computes a purchase metric by analyzing the difference between release and purchase dates, and release and purchase prices of devices, and uses this metric to generate a purchase metric threshold for recommending devices to users, taking into account their past purchasing behavior and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing recommendation systems use basic price or specification matching, then the system complexity remains low, but the recommendation accuracy is insufficient
Solution Approach 1:
The patent transforms the recommendation approach by changing from basic parameter matching (price, specifications) to using derived behavioral parameters (purchase metric, purchase delay, preferred discount). These new parameters are calculated from historical purchase data and provide deeper insights into user preferences, thereby improving recommendation accuracy while maintaining manageable system complexity through systematic parameter transformation.
Solution Approach 2:
The patent introduces intermediate computational layers (purchase metric calculation, purchase delay analysis, preferred discount determination) that mediate between raw purchase data and final recommendations. These intermediary processing steps extract meaningful patterns from historical data, enabling accurate recommendations without requiring direct complex analysis of all raw data points.
2Measurement precision
If the system tracks detailed purchase history and computes purchase metrics, then recommendation accuracy improves, but data processing requirements increase
Solution Approach 1:
The patent performs preliminary processing of purchase data by pre-calculating and storing key metrics (purchase metric, purchase delay, preferred discount) from historical purchase history. This preliminary action transforms raw data into meaningful indicators in advance, reducing the computational burden during actual recommendation generation and minimizing data processing time when recommendations are needed.
3Adaptability or versatility
If the system uses purchase delay and preferred discount metrics, then user preference alignment improves, but the complexity of computing these metrics increases
Solution Approach 1:
The patent segments the complex task of understanding user preferences into distinct computational components: purchase delay calculation (time between device release and purchase), preferred discount determination (discount amount or percentage), and purchase metric synthesis. This segmentation allows each aspect to be computed independently using straightforward formulas, reducing overall computational complexity while maintaining comprehensive user preference analysis.
Data Source
AI summary
Identification information related to a device may be received that includes user information and device information. Based on the user information, a user entry corresponding to the user information may be located in a database of user records. It may be determined that the device information is not found in the user entry. A plurality of sales attributes may be determined in response. Those may include at least one of (1) a release date of the device and a purchase date indicating when the user purchased the device, and (2) a release price of the device and a current price of the device at the purchase date. Based on the information, a purchase metric may be computed and the device information stored in the previously purchased device field of the entry. Based on the purchase metric, a purchase metric threshold may be generated for recommending another device to the user.