Gift Recommendation Server Using Recipient Segmentation
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Solution Overview
Problem
Existing product recommendation systems struggle to provide customized gift recommendations that accurately match the tastes and interests of individual gift recipients, often relying on general public tastes rather than specific recipient information.
Innovation Solution
A method for product recommendation that involves a server receiving a gift recommendation request, checking available basic information of the gift recipient, and providing customized recommendation information to the gift sender. This information includes prioritized recommendations based on the recipient's specific information and additional recommendations not based on this information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a product recommendation system uses general public taste information, then it can provide recommendations without requiring recipient information, but it cannot provide customized recommendations that match individual recipient tastes
Solution Approach 1:
The patent segments recommendation information into two distinct categories: first recommendation information based on recipient's basic information (enabling customization) and second recommendation information not based on basic information (maintaining simplicity). This segmentation allows the system to provide both customized and general recommendations without requiring the user to choose one approach over the other.
Solution Approach 2:
The recommendation system is designed to handle multiple types of information sources simultaneously - it can process recipient basic information when available, fallback to general public taste information when basic information is unavailable, and provide both types of recommendations in a unified interface. This multi-functionality resolves the contradiction by making the system adaptable to different information availability scenarios.
2Adaptability or versatility
If a product recommendation system collects and uses recipient's basic information, then it can provide customized recommendations, but it increases the complexity of information management and privacy handling
Solution Approach 1:
The patent extracts only the necessary basic information from the recipient's profile (such as gender, age, interests) that is sufficient for generating customized recommendations, rather than requiring or processing all possible personal information. This extraction approach enables personalization while minimizing information management burden and privacy concerns.
Solution Approach 2:
The system performs preliminary processing of recipient basic information by the server before generating recommendations, organizing and structuring the information in advance. This preliminary action reduces the information management burden during the actual recommendation generation process, as the server has already prepared the basic information for efficient processing.
3Ease of operation
If a product recommendation system provides only popular gifts, then it simplifies the recommendation process, but it fails to match the specific tastes and interests of the gift recipient
Solution Approach 1:
The patent applies local quality by providing different types of recommendation information with different characteristics: first recommendation information is tailored to the specific recipient's tastes and interests (high precision, customized), while second recommendation information represents general popular gifts (high simplicity, broad appeal). Both types are provided together, allowing the sender to choose based on their needs.
Solution Approach 2:
The system goes beyond providing only popular gifts by additionally providing customized recommendations based on recipient basic information. This excessive action (providing more than the minimum popular recommendations) ensures that at least some recommendations will highly match the recipient's tastes, while the popular gift recommendations maintain the simplicity aspect.
Data Source
AI summary
Proposed is a method for product recommendation by a server. The method may include receiving a gift recommendation request from a first user terminal for a second user, checking available basic information of the second user, and determining a first recommendation information based on the basic information, wherein the first recommendation information comprises a first recommendation reason and at least one product related to the first recommendation reason. The method may also include determining a second recommendation information not based on the basic information, wherein the second recommendation information comprises a second recommendation reason and at least one product related to the second recommendation reason. The method may further include providing recommendation information comprising the first and second recommendation information to the first user terminal. The first recommendation information may be prioritized for display over the second recommendation information.