Notebook to try out MMR on a fashion data set #470
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Found 2 changed notebooks. Review the changes at https://app.gitnotebooks.com/elastic/elasticsearch-labs/pull/470 |
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…ation on meta information such as brands.
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ioanatia
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ioanatia
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the code examples make sense to me - just had some suggestions on how we can render the preview more nicely
| "3. Search items with a broad search term\n", | ||
| "4. Apply result diversification with the MMR algorithm to the results.\n", | ||
| "\n", | ||
| "Check out our blog post on this topic to learn more about " |
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I guess we will update this later with the actual link? once the blog post is available?
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I am adding the link as it will be, that way we don't forget 👍
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Requirement already satisfied: anyio==4.9.0 in ./venv/lib/python3.12/site-packages (from -r requirements.txt (line 1)) (4.9.0)\n", |
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can we remove these outputs? we can just modify the file and remove these strings from the list.
they don't render nicely in the preview:
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## 9. Reranking with Maximum Marginal Relevance (MMR)\\n\\nMMR is a diversity-promoting algorithm that balances:\\n- **Relevance**: How well items match the query\\n- **Diversity**: How different items are from each other\\n\\nThe algorithm iteratively selects items that are relevant to the query but different from already selected items." |
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this does not render that well in the preview:
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Notebook for search lab article: https://github.com/elastic/search-labs-elastic-co/issues/1594