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Evgeniya Sukhodolskaya
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qdrant-landing/content/articles/relevance-feedback.md
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@@ -419,7 +419,7 @@ For example, Relevance Feedback Query can be a great aid for search agents, lett
### How to Use It
For ease of use, as one shouldn't need a machine learning degree to use a new feature, we published a [Python package that customizes Naive Formula weights for your dataset, retriever, and feedback model](https://pypi.org/project/relevance-feedback/).
For ease of use, as one shouldn't need a machine learning degree to use a new feature, we published a [Python package that customizes Naive Formula weights for your dataset, retriever, and feedback model](https://github.com/qdrant/relevance-feedback).
What you need is a Qdrant collection, an idea of which feedback model you'd like to use to guide your retriever, and, optionally, a small set of use case-specific queries (50–300).
@@ -438,7 +438,7 @@ Once you've obtained the weights, simply plug them into your [Qdrant Client of c
### Evaluating Your Gains
Additionally, the [Relevance Feedback Parameters package](https://pypi.org/project/relevance-feedback/) provides an `Evaluator` module with two metrics: **relative gain** based on the **abovethreshold@N** metric from the "Experiments" section above, and a metric more recognizable to people in search -- **Discounted Cumulative Gain (DCG) Win Rate**.
Additionally, the [Relevance Feedback Parameters package](https://github.com/qdrant/relevance-feedback) provides an `Evaluator` module with two metrics: **relative gain** based on the **abovethreshold@N** metric from the "Experiments" section above, and a metric more recognizable to people in search -- **Discounted Cumulative Gain (DCG) Win Rate**.
- **Discounted Cumulative Gain (DCG) Win Rate**
For each query, we compute DCG@N for both compared methods (vanilla and relevance feedback-based retrieval) against ground truth relevancy scores from a feedback model. The method with the higher DCG@N gets a "win".