Personalized Recommendation System Using Segmentation and Preliminary Action
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing item recommendation systems on e-commerce platforms often provide non-personalized recommendations, leading to customer disengagement due to irrelevant items, resulting in decreased satisfaction and purchase likelihood.
Innovation Solution
A system that uses machine learning to determine and display personalized customer insights, including preferences and favorites, by analyzing historical purchases and interactions, to present relevant product types and attributes tailored to individual customers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If non-personalized recommendations are provided to all customers, then the recommendation system is simple to implement and maintain, but customer engagement and satisfaction decrease due to irrelevant items
Solution Approach 1:
The patent segments customers into different groups based on their shopping behaviors, preferences, and historical data. By dividing the customer base into distinct segments, the system can provide personalized recommendations to each segment rather than treating all customers uniformly, thereby improving engagement while maintaining manageable system complexity
Solution Approach 2:
The system performs preliminary analysis of customer data, shopping habits, and preferences in advance to pre-segment customers and pre-generate personalized recommendation profiles. This preliminary action allows the recommendation system to quickly serve personalized content without complex real-time processing, balancing operational simplicity with personalized engagement
2Reliability
If personalized recommendations are provided based on customer habits, then customer engagement and satisfaction increase, but the system complexity increases due to data processing requirements
Solution Approach 1:
The patent introduces intermediary components such as machine learning models and data processing layers that mediate between raw customer data and recommendation outputs. These intermediaries automatically process and analyze customer behaviors, preferences, and shopping patterns to generate personalized recommendations, reducing the apparent system complexity while maintaining high personalization quality
Solution Approach 2:
The recommendation system performs self-service by automatically collecting, analyzing, and processing customer data without requiring manual intervention. The system autonomously identifies customer patterns, updates preferences, and generates personalized recommendations, thereby managing complexity internally while providing simple, effective personalized service to customers
3Stability of the object's composition
If repeated recommendations of the same items are provided, then the recommendation system maintains consistency, but customer interest is lost leading to churn
Solution Approach 1:
The patent implements dynamic recommendation strategies that adapt to changing customer preferences and behaviors over time. The system continuously monitors customer interactions, updates preference profiles, and adjusts recommendations accordingly, ensuring both consistency in delivering relevant items and variety in preventing customer fatigue and churn
Data Source
AI summary
This application relates to apparatus and methods for automatically determining and providing digital customer insights based on historical customer data. In some examples, a computing device obtains user data for a user. In response, the computing device receives a plurality of product types relevant to the user and their corresponding relevance scores. For each product type, the computing device then receives a set of attributes, where each attribute is associated with an affinity score for the user. The computing device determines, for each product type, an overall score for each attribute and product type pair based on the relevance score for the product type and the affinity score for the corresponding attribute. At least one attribute and product type pair is presented to the user based on the corresponding overall score.