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21 lines
948 B
Markdown
21 lines
948 B
Markdown
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# Semantic Index
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## Evaluation
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### Metrics
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nDCG@k:
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- "The value of NDCG is determined by comparing the relevance of the items returned by the search engine to the relevance of the item that a hypothetical "ideal" search engine would return.
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- "The relevance of result is represented by a score (also known as a 'grade') that is assigned to the search query. The scores of these results are then discounted based on their position in the search results -- did they get recommended first or last?"
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MRR@k:
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- "Mean reciprocal rank quantifies the rank of the first relevant item found in teh recommendation list."
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MAP@k:
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- "Mean average precision averages the precision@k metric at each relevant item position in the recommendation list.
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Resources:
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- [Evaluating recommendation metrics](https://www.shaped.ai/blog/evaluating-recommendation-systems-map-mmr-ndcg)
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- [Math Walkthrough](https://towardsdatascience.com/demystifying-ndcg-bee3be58cfe0)
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