Dynamic Content Recommendation via User Feature Tags
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Solution Overview
Problem
Current content recommendation systems fail to provide personalized recommendations as they combine different types of content in fixed proportions and orders, not accounting for individual user preferences and behavior, leading to suboptimal user engagement.
Innovation Solution
A method and apparatus for content recommendation that utilizes user feature tags generated from basic attribute and behavior information to determine personalized content proportions and orders, with a recommendation model trained on historical data and updated periodically, and a small-stream experiment to replace models for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed proportion and order content combination is used, then system complexity is reduced, but recommendation accuracy deteriorates
Solution Approach 1:
The patent implements dynamic content combination by using a recommendation model that processes user feature tags to generate personalized content proportions and sequences. The system transitions from static fixed combinations to dynamic adaptive combinations based on real-time user characteristics, resolving the contradiction between system simplicity and recommendation accuracy.
Solution Approach 2:
The patent applies local quality by customizing content recommendations for different user segments. Each user receives a tailored content combination based on their specific feature tags (e.g., reading habits, interests, behavior patterns), rather than a uniform fixed combination. This allows the system to maintain simplicity in the overall framework while achieving high accuracy through localized personalization.
2Measurement precision
If personalized recommendation is implemented, then recommendation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the recommendation system into distinct functional modules: user feature tag extraction module, recommendation model module, and content selection module. This segmentation allows personalized recommendations to be implemented through coordinated simple operations in each module, rather than requiring complex monolithic processing, thus improving accuracy while managing device complexity.
Solution Approach 2:
The patent performs preliminary action by pre-extracting user feature tags from user data before the actual recommendation process. User profiles, reading habits, and interest tags are prepared in advance, so that during content recommendation, the system only needs to match these pre-processed features with content, reducing real-time computational complexity while maintaining personalization accuracy.
3Measurement precision
If model updates are performed frequently, then recommendation accuracy is improved, but energy consumption increases
Solution Approach 1:
The patent implements periodic model updates rather than continuous updates. The recommendation model is trained and updated at scheduled intervals using accumulated user feedback and new data, rather than constantly retraining. This periodic approach maintains recommendation accuracy by incorporating fresh data while significantly reducing energy consumption compared to frequent continuous updates.
Data Source
AI summary
A method, an apparatus for content recommendation, an electronic device and a storage medium are disclosed. The method may include: for a user to be recommended, obtaining candidate contents to be recommended and at least one user feature tag for the user; determining a recommendation scheme according to the candidate contents and the at least one user feature tag, including respective proportions of different types of candidate contents in N pieces of recommended contents recommended to the user, the N pieces of recommended contents selected from the candidate contents according to the proportions, and an order of displaying the recommended contents in the N pieces of recommended contents, N is a positive integer greater than one and less than or equal to the number of candidate contents; and returning the recommended contents to the user according to the recommendation scheme.