Product Recommendation Using Preference Gradient Similarity
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Solution Overview
Problem
Existing recommendation systems fail to accurately account for the change trends in user preferences, leading to inaccurate product recommendations due to the lack of consideration of similarity dynamics between users and items.
Innovation Solution
A product recommendation method that determines a target product potential expectation value and gradient value to analyze preference changes, forming a similar user set based on these values, and recommending products based on this analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommendation methods are used, then the system can provide basic product recommendations, but the accuracy of recommendations does not match user's evolving preferences
Solution Approach 1:
The patent introduces dynamic gradient values that continuously track changes in user preferences over time. The gradient value represents the rate of change of user preference for products, allowing the system to adapt to evolving user interests rather than relying on static historical data. This dynamic approach resolves the contradiction by making the recommendation system responsive to preference changes while maintaining accuracy.
Solution Approach 2:
The patent transforms the recommendation approach by changing from using static user-profile parameters to dynamic gradient parameters that capture preference changes. The gradient value (a mathematical parameter representing rate of change) is introduced to quantify how user preferences evolve, enabling the system to adapt recommendations to current user interests rather than historical patterns alone.
2Measurement precision
If the system considers user preference change trends, then recommendation accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent extracts only the essential feature needed for tracking preference changes - the gradient value representing the rate of change. Rather than analyzing all aspects of user behavior in detail, the system focuses on extracting the key dynamic parameter (gradient) that captures preference evolution, thereby reducing computational complexity while maintaining recommendation accuracy.
Solution Approach 2:
The patent simplifies the complexity by transforming complex user preference data into a single meaningful parameter - the gradient value. This parameter transformation converts multidimensional user behavior analysis into a manageable scalar value that represents the essential dynamic characteristic (preference change rate), making the system computationally efficient while accurate.
3Productivity
If static user profiles are used for recommendations, then the system is computationally efficient, but it cannot capture dynamic user preferences
Solution Approach 1:
The patent replaces static user profile parameters with dynamic gradient parameters that automatically update as user preferences change. The gradient value is computed from sequential user interactions and continuously reflects current preference trends, enabling the system to maintain processing efficiency while capturing dynamic preferences that static profiles cannot represent.
Data Source
AI summary
Provided are a product recommendation method and apparatus, a computer storage medium, and system. The method comprises: determining a target product potential expectation value and a target gradient value of a target user; on the basis of the target product potential expectation value and the target gradient value, determining a similar user set; in the similar user set, acquiring a plurality of gradient values corresponding to a plurality of products to be recommended for each user, and composing a similar user gradient value set by using the plurality of gradient values; and determining at least one recommended gradient value from the similar user gradient value set, and recommending at least one product to be recommended corresponding to at least one recommended gradient value to the target user.