Database Management for Tracking Recommendation Effectiveness
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Solution Overview
Problem
Existing systems for recommending products through web sites fail to track user actions after recommendations are made, making it difficult to measure the effectiveness of recommendations.
Innovation Solution
A database management device and method that acquires and registers recommendation data, including user interactions with recommended web pages, allowing for the tracking of user actions and measurement of recommendation effectiveness by associating the recommender, recommended object, and user actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If recommendation systems are implemented through web sites, then users can receive product recommendations, but it becomes difficult to track user actions after receiving recommendations and measure recommendation effectiveness
Solution Approach 1:
The system performs preliminary actions by embedding tracking codes in recommendation emails before they are sent out. When users click on recommended products or make purchases, the pre-placed tracking mechanisms automatically capture these actions. This allows the system to track user behavior without adding complex tracking infrastructure at the point of interaction.
Solution Approach 2:
The patent introduces an intermediary database that stores recommendation information including user IDs, product IDs, and tracking codes. This intermediary structure acts as a mediator between the recommendation system and user actions, enabling tracking without directly complicating the recommendation delivery mechanism. The database serves as a central hub that connects recommenders, recommended items, and user responses.
2Measurement precision
If tracking mechanisms are added to monitor user actions, then recommendation effectiveness can be measured, but the system complexity increases
Solution Approach 1:
The patent merges the tracking function with the existing recommendation email system by integrating tracking codes directly into the recommendation data structure. Instead of adding separate tracking infrastructure, the system combines recommendation delivery and action tracking into a unified process. The recommendation email itself becomes the tracking vehicle through embedded codes.
Solution Approach 2:
The system implements self-service tracking where users automatically generate tracking data through their natural interactions with recommended products. When users click on product links or make purchases, their actions automatically update the recommendation database without requiring additional tracking software or user participation. The users themselves serve as the tracking mechanism through their documented actions.
Data Source
AI summary
A server (11) includes a history registration unit (73) that acquires information of a recommendation email for recommending a specified recommended object from a first user to a second user, generates recommendation data in which the first user, the second user and a web page of the recommended object are associated based on the information, and registers the recommendation data into a history database (63), an operation information acquisition unit (76) that acquires operation information indicating operation performed by the second user on the web page of the recommended object in response to the recommendation email, and a history update unit (77) that specifies an action of the second user taken on the recommended object based on the operation information and adds action information indicating the specified action to the corresponding recommendation data.