Customized Product Ranking via Demographic Segmentation
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Solution Overview
Problem
On-line shopping malls struggle to provide users with customized product information, as existing methods rely on sales volume, which does not account for user preferences based on demographics like age and gender, making it difficult for users to find relevant popular products.
Innovation Solution
A method that utilizes a database of shopping activity data to prioritize products based on user groups, where user profiling information such as age and gender is used to determine group membership, and computes a score for each product by multiplying accumulated shopping activity counts with preassigned weights, resulting in a customized list of popular products displayed to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If product information is provided based on sales volume, then popular products are easily identified, but the information is not customized to user preferences and demographics
Solution Approach 1:
The patent segments users into different demographic groups (age, gender, location) and segments products by category and attributes. This segmentation allows the system to provide customized popular product lists tailored to each user's demographic characteristics, resolving the contradiction between identifying popular products and customizing to user preferences.
Solution Approach 2:
The patent applies local quality by customizing product recommendations to specific user demographics. Instead of providing a universal popular products list, the system generates different popular product lists for different demographic groups (e.g., teenagers vs. adults), making the information locally optimized for each user's preferences.
2Adaptability or versatility
If a comprehensive product search process is provided, then users can find any product, but it takes time and requires manual searching without helpful guidance
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing popular product lists for different demographic groups before users actually search. The system proactively generates and maintains these customized lists based on historical sales data and user behavior patterns, so when a user accesses the system, the information is already prepared and ready for immediate display.
Solution Approach 2:
The patent incorporates feedback mechanisms by continuously monitoring user interactions with products (views, purchases, preferences) and using this feedback to refine and update the popular product lists. This feedback loop ensures the system learns from user behavior and improves its recommendations over time, making the search more efficient and accurate.
3Adaptability or versatility
If user profiling data is collected and processed, then customized product information can be provided, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer in the form of a centralized processing system that handles data collection, storage, and analysis. This intermediary component (the server-side processing system) acts as a mediator between the simple user interface and the complex data processing requirements, allowing the system to provide customization without exposing the complexity to end users.
Solution Approach 2:
The patent implements self-service by having the system automatically collect, process, and utilize user profiling data without requiring manual input or complex user interaction. The system self-updates user profiles based on browsing behavior and automatically generates customized recommendations, reducing the operational complexity burden on users while maintaining high customization capability.
Data Source
AI summary
Disclosed is a method of providing customized product information. The method includes maintaining a basic index database for recording basic index data associated with a plurality of users for each product model, extracting the basic index data of the user corresponding to a predetermined classification standard from the basic index database, generating point information associated with each product model by calculating the extracted basic index data, and displaying each product model on a predetermined webpage according to the generated point information.