Recommendation Data Presentation via Pre-computed User Profiles
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Solution Overview
Problem
Existing schemes for presenting commodity recommendation data are inaccurate in matching user search intentions and fail to efficiently satisfy user search demands, as they rely solely on search history and require users to manually input keywords.
Innovation Solution
A method and apparatus for presenting recommendation data that involves displaying a first search middle page after a trigger operation on a search box, obtaining commodity information of related commodities based on content genres, and presenting this information in a preset area of the first search middle page, thereby reducing the user's search path and improving search efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If commodity recommendation data is presented based on search history, then the system can provide basic recommendation functionality, but the accuracy of matching user search intentions deteriorates
Solution Approach 1:
The system performs preliminary analysis of user behavior data (browsing history, search history, purchase records) before the user actually searches, pre-calculating and storing user profiles, interest labels, and commodity preferences. When a search occurs, the system quickly retrieves pre-prepared recommendation data instead of computing in real-time, thereby improving both matching accuracy and response speed without requiring additional user operations.
Solution Approach 2:
The system implements a feedback mechanism where user interactions with recommended commodities (clicks, views, purchases, dwell time) are continuously collected and used to refine user profiles and commodity models. This closed-loop feedback improves the accuracy of intention matching over time, while the system learns from actual user behavior rather than relying solely on explicit search queries.
2Measurement precision
If users manually input keywords to search for similar commodities, then search specificity can be improved, but the complexity of the search process increases
Solution Approach 1:
The system performs self-service by automatically analyzing the current commodity context (commodity ID, attributes, category) and generating relevant search queries and recommendation lists without requiring user input. The system autonomously retrieves similar commodities, frequently purchased together items, and replacement options, then presents them to the user who simply needs to browse and select, eliminating the need for manual keyword input while maintaining high search specificity.
3Reliability
If comprehensive commodity information is collected and analyzed, then recommendation accuracy can be improved, but the processing time and system complexity increase
Solution Approach 1:
The system performs comprehensive data collection and analysis in advance by building detailed user profiles from browsing history, search queries, purchase records, and commodity preferences during normal operations. Commodity models including attributes, categories, and relationship graphs are pre-computed and stored in databases. When a recommendation is needed, the system quickly queries these pre-prepared structures rather than analyzing raw data in real-time, achieving both high accuracy and fast response.
Solution Approach 2:
The recommendation system is segmented into independent modules: user profile analysis, commodity modeling, similarity calculation, and result ranking. Each module processes specific aspects of the data independently and passes results to the next stage. This modular segmentation allows parallel processing of different data types and reduces the time complexity of comprehensive analysis while maintaining recommendation accuracy through coordinated module outputs.
Data Source
AI summary
The disclosure provides a method and apparatus for presenting recommendation data, a computer device, and a storage medium. The method includes: presenting a first search middle page in response to a trigger operation on a search box in a target page; obtaining commodity information of a related commodity of a target commodity presented in the target page; and presenting the commodity information of the related commodity in a preset area of the first search middle page.