Dynamic Matching Model for Object Recommendation
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Solution Overview
Problem
Existing object recommendation systems fail to dynamically adjust their matching models based on actual application effects, leading to suboptimal recommendation performance over time.
Innovation Solution
The method involves obtaining a first user profile based on historical behavior data, using a matching model to recommend objects, updating the model based on subsequent user behavior after the recommendation, and refining the model using both profiles and the recommended object to enhance matching accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a matching model is used for object recommendation, then recommendation performance is improved, but the model cannot dynamically adapt to changing user behavior over time
Solution Approach 1:
The patent implements a feedback mechanism where user interaction data (clicks, views, purchases) is continuously collected after recommendations are made. This feedback data is then used to update and retrain the matching model, creating a closed-loop system that adapts to changing user preferences over time while maintaining reliable recommendation performance
Solution Approach 2:
The patent transforms the static matching model into a dynamic system by implementing continuous model updates based on incoming user behavior data. The model evolves over time through periodic retraining and parameter adjustments, enabling it to adapt to temporal changes in user preferences while preserving its core recommendation functionality
2Measurement precision
If user behavior data is collected over time for model updates, then model accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the data collection and model update process into distinct modular components: data collection module, data processing module, model training module, and deployment module. Each component handles a specific aspect of the pipeline, making the overall complex system manageable and maintainable while achieving high model accuracy through continuous refinement
Data Source
AI summary
An object recommendation method, computer device, and medium are provided, relating to the field of artificial intelligence and, particularly, content recommendation. A method includes: obtaining a first user profile of a user, the first user profile being determined based on behavior data of the user over a first historical period of time; using a matching model to determine a recommended object based on the first user profile; recommending the recommended object to the user; obtaining a second user profile of the user, the second user profile being determined based on behavior data of the user over a second historical period of time, and the behavior data of the user over the second historical period of time includes behavior data of the user after the recommended object is recommended to the user; and updating the matching model based on the first user profile, the second user profile, and the recommended object.