Entity Ranking via Distribution Sampling for Low-Interaction Visibility
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Solution Overview
Problem
Traditional methods for selecting and ranking entities in machine learning models tend to favor entities with higher interaction levels, leading to lower-quality entities with fewer interactions being overlooked, which limits their visibility and future traction.
Innovation Solution
The use of distribution sampling methods to generate and sample rate distributions and probability distributions, allowing entities with lower interaction levels to be more prominently selected and ranked, thereby increasing their visibility and interaction opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ranking methods are used that favor entities with higher interaction levels, then entities with high interactions are ranked higher and receive more visibility, but entities with lower interactions are overlooked and receive reduced visibility, creating a feedback loop that prevents quality improvement
Solution Approach 1:
The system performs preliminary actions by generating synthetic training data that includes low-interaction entities before the actual ranking process. This pre-prepared data ensures that low-interaction entities have a chance to be ranked appropriately, breaking the traditional feedback loop where only high-interaction entities gain visibility.
Solution Approach 2:
The system changes the parameter of interaction level weighting in the ranking process. Instead of directly using observed interaction counts, it applies learned parameters from training data that adjust the weight given to interaction levels, allowing low-interaction entities to receive fairer ranking scores and increased visibility.
2Adaptability or versatility
If distribution sampling methods are applied to increase visibility of low-interaction entities, then more entities receive interaction opportunities, but the complexity of the ranking system increases due to distribution generation and sampling processes
Solution Approach 1:
The system performs preliminary action by pre-generating distribution sampling data during the training phase. This pre-computed training data encapsulates the complex distribution sampling logic, so that during actual operation, the system only needs to apply pre-learned patterns rather than performing complex distribution generation and sampling in real-time, thus reducing operational complexity.
3Reliability
If more training data is generated through distribution sampling to improve model performance, then the quality of machine learning model output improves, but the time and computational resources required for data generation and model training increase
Solution Approach 1:
The system performs preliminary action by generating and preparing the distribution sampling training data in advance during an offline training phase. This pre-computed training data is then used to train the machine learning model before deployment. During actual operation, the model applies pre-learned patterns without requiring real-time distribution sampling, thus reducing online computational overhead and time loss.
Data Source
AI summary
Embodiments of the disclosed technologies include generating a reward score for an entity. A rate distribution is determined using the reward score. A sampled rate value is generated by sampling the rate distribution. A probability score is generated for a pair of the entity and a user using the sampled rate value. A probability distribution is determined using the probability score. A sampled probability value is generated by sampling the probability distribution. A machine learning model is trained using the sampled probability value.