Personalized Recommendation System Using Dynamic Customer Segmentation
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Solution Overview
Problem
Current product recommendation systems in e-commerce fail to effectively personalize recommendations for individual customers, leading to lower purchase effectiveness due to the assumption that customers with similar purchase behavior patterns have identical preferences, resulting in inadequate product recommendations for customers with unique patterns.
Innovation Solution
A personalized recommendation system that employs a combination of single recommendation algorithms, such as collaborative filtering, association rule, and purchase pattern algorithms, along with a hybrid approach, to analyze and prioritize product recommendations based on performance evaluations, ensuring higher hit rates by selectively collecting and processing relevant customer data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If collaborative filtering algorithm is used to recommend products based on similar purchase behavior patterns, then recommendation coverage is improved, but recommendation precision for customers with unique patterns deteriorates
Solution Approach 1:
The patent segments customers into different groups based on their purchase behavior patterns (e.g., impulse buyers, planned buyers, browse-and-buy customers). Different recommendation algorithms are then applied to different customer segments: collaborative filtering for customers with similar patterns and alternative algorithms for customers with unique patterns. This segmentation allows the system to maintain broad coverage while improving precision for each segment.
Solution Approach 2:
The system dynamically adjusts the recommendation approach based on real-time analysis of customer purchase behavior patterns. When a customer's behavior deviates from expected patterns, the system switches from collaborative filtering to alternative algorithms. This dynamic adaptation enables the system to maintain both coverage and precision by responding to individual customer needs as they evolve.
2Device complexity
If product recommendations are made en bloc to every customer without considering individual preferences, then system complexity is reduced, but purchase effectiveness deteriorates
Solution Approach 1:
The system employs dynamic classification of customers into segments based on their purchase behavior patterns. This dynamic segmentation allows the system to maintain relative simplicity while significantly improving purchase effectiveness. By automatically classifying customers and applying appropriate algorithms, the system achieves personalization without requiring complex manual configuration for each customer.
Solution Approach 2:
The patent changes the parameters of customer classification and algorithm selection based on observed purchase behavior patterns. The system monitors various parameters (purchase frequency, basket composition, timing patterns) and adjusts the recommendation strategy accordingly. This parameter-based approach enables the system to balance complexity and effectiveness by using measurable customer characteristics to guide algorithm selection.
3Device complexity
If collaborative filtering assumes customers with similar patterns have identical preferences, then algorithm simplicity is maintained, but recommendation accuracy for diverse customers deteriorates
Solution Approach 1:
The patent segments customers into distinct groups based on their purchase behavior patterns, allowing each segment to be handled with appropriate algorithms. This segmentation resolves the contradiction by maintaining simplicity within homogeneous segments (where CF works well) while applying more sophisticated algorithms to heterogeneous segments, thereby improving overall accuracy without excessive complexity.
Solution Approach 2:
The system merges multiple recommendation algorithms into a unified framework that can handle different customer types. By combining collaborative filtering with alternative algorithms (such as content-based filtering or association rules) and merging their outputs through a coordinated selection mechanism, the system achieves high accuracy for diverse customers while maintaining a cohesive, manageable algorithm structure.
Data Source
AI summary
Provided is a method, system, and a computer-readable record medium for providing a personalized recommendation of products. The method of providing a personalized recommendation of products may include obtaining a first recommendation result using each of two or more single recommendation algorithms, performing a first performance evaluation, using a processor, with respect to the first recommendation result from each of the single recommendation algorithms, obtaining a second recommendation result based on the first recommendation result from each of the two or more single recommendation algorithms using a hybrid recommendation algorithm, the hybrid recommendation algorithms being different than each of the two or more single recommendation algorithms, performing a second performance evaluation, using the processor, with respect to the second recommendation result from the hybrid recommendation algorithm, and listing product recommendations after selecting a recommendation algorithm having a priority using the first performance evaluation and the second performance evaluation.