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grammar formatting
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@@ -45,7 +45,7 @@ User_2: [ 0, 0, 0, 0, 4, 0, 0, 2, 0, 0 ]
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Comparing two sparse representations, you'd be usually interested in how much they agree on the same features/objects (e.g., the same movie rating).
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Comparing two sparse representations, you'd be usually interested in how much they agree on the same features/objects (e.g., the same movie rating).
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The **dot product** distance metric, introduced in the **day 1** (*Vector Search Fundamentals/Distance Metrics*), is a perfect fit for measuring the similarity between sparse representations.
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The **dot product** distance metric, introduced in the **Day 1** (*Vector Search Fundamentals/Distance Metrics*), is a perfect fit for measuring the similarity between sparse representations.
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It multiplies corresponding dimensions and summes the results.
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It multiplies corresponding dimensions and summes the results.
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```text
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```text
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