Recommendation Evaluation Using Visit Likelihood Baselines
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Solution Overview
Problem
Existing recommendation systems fail to effectively evaluate the effectiveness of recommendations, leading to repetitive transmission of ineffective recommendations.
Innovation Solution
A recommendation evaluation device that includes an evaluation derivation unit to derive visit likelihood evaluations for recommended and non-recommended stores, using a recommendation evaluation unit to assess the recommendation effect based on these evaluations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If recommendation is transmitted repeatedly to users, then the recommendation coverage and user engagement increase, but the effectiveness of recommendations cannot be evaluated and ineffective recommendations are transmitted repeatedly
Solution Approach 1:
The patent implements a feedback mechanism by deriving visit likelihood evaluations both with and without recommendation, then comparing these evaluations to determine recommendation effectiveness. This feedback loop allows the system to learn from past recommendations and adjust future recommendation strategies, preventing repeated transmission of ineffective recommendations while maintaining high transmission efficiency for effective ones.
Solution Approach 2:
The patent performs preliminary evaluation by deriving visit likelihood evaluations assuming no recommendation was made, before comparing with actual visit likelihood evaluations. This preliminary action establishes a baseline for what would have happened without intervention, enabling accurate assessment of recommendation effectiveness and informing future recommendation decisions.
2Measurement precision
If visit likelihood evaluation is derived for both recommended and non-recommended scenarios, then recommendation effectiveness can be accurately evaluated, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the evaluation process into distinct components: deriving visit likelihood evaluation for recommended stores, deriving visit likelihood evaluation assuming no recommendation, and comparing these evaluations. This segmentation allows each component to be processed independently and efficiently, reducing overall computational complexity while maintaining high measurement precision through systematic comparison of the segmented evaluation results.
Data Source
AI summary
An object is to provide a recommendation evaluation device capable of evaluating a recommendation. A recommendation system 100 of the present disclosure includes an evaluation derivation unit 103 configured to derive a visit likelihood evaluation g(x) for a store that has been recommended to a target user and a visit likelihood evaluation g(x) assuming that no recommendation has been made, and a recommendation evaluation unit 104 configured to derive a recommendation evaluation on the basis of the visit likelihood evaluation g(x) for the store that has been recommended and the visit likelihood evaluation assuming that no recommendation has been made.