Dynamic User Profile Modification for Recommendation Accuracy
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Solution Overview
Problem
Existing recommendation algorithms for industry professionals rely on binary yes/no signals and static user profiles, failing to accurately adapt to continuous user interactions, leading to less tailored recommendations.
Innovation Solution
A method to modify user profiles dynamically based on tracked interactions with items, such as annotations, highlighting, and modifications, allowing for a continuum of user feedback to refine recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If binary yes/no scoring is used for user actions, then the recommendation system is simple to implement, but the accuracy of user profiling is insufficient
Solution Approach 1:
The patent transforms the binary scoring system into a multi-parameter continuous spectrum system. Instead of simple yes/no scores, the system captures multiple interaction parameters (time spent, number of interactions, depth of engagement) that continuously update user profiles, thereby improving profiling accuracy while maintaining implementation feasibility through systematic parameter collection.
Solution Approach 2:
The invention adds temporal and intensity dimensions to user interaction tracking. By measuring not just whether an action occurred but how long it took, how many times it occurred, and the depth of engagement, the system transitions from one-dimensional binary scoring to multi-dimensional continuous measurement, significantly enhancing user profile accuracy.
2Productivity
If static user profiles are used, then the recommendation algorithm is computationally efficient, but the adaptability to evolving user preferences is poor
Solution Approach 1:
The patent implements dynamic user profiles that continuously evolve based on real-time interaction data. User profiles are no longer static but are continuously updated with new interaction parameters, allowing the recommendation system to adapt to changing user preferences while maintaining computational efficiency through incremental updates rather than complete recalculations.
Solution Approach 2:
The system incorporates continuous feedback loops where user interactions are tracked, analyzed, and used to update user profiles in real-time. This feedback mechanism ensures that the recommendation algorithm continuously adapts to evolving user preferences, transforming static profiles into dynamic, responsive representations of user interests.
3Measurement precision
If continuous user interaction tracking is implemented, then the recommendation accuracy improves, but the system complexity increases
Solution Approach 1:
The patent creates a universal tracking framework that captures multiple types of user interactions through a unified system. The same infrastructure tracks various interaction types (views, clicks, time spent, engagement depth) across different content types, reducing overall system complexity by avoiding separate tracking mechanisms for each interaction type while maintaining high recommendation accuracy.
Data Source
AI summary
Methods and apparatus for modifying a user profile for a recommendation algorithm are provided. A user is provided with electronic access to an item. The item may comprise one of a document, an article, a chart, a graphic, a report, a web page, or the like. User interaction with the item is enabled. The user interaction with the item is then electronically tracked and stored. The user's user profile used by a recommendation engine is then modified based on the tracked user interactions. The user interaction may comprise at least one of annotating, highlighting, modifying, customizing, adding comments to the item, and the like. The user modified item can be saved and details of the user interaction with the item may be used to modify the user profile. At least one of items or peer recommendations can then be provided to the user based on the modified user profile.