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[Mike] Fix link
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@ -462,7 +462,7 @@ In more advanced search systems, the the cosine similarity of embeddings can be
Recommendations are quite similar to search, except that instead of a free-form text query, the inputs are items in a set. And instead of using pairs of doc-query models, you can use a single symmetric similarity model (e.g., `text-similarity-curie-001`). Recommendations are quite similar to search, except that instead of a free-form text query, the inputs are items in a set. And instead of using pairs of doc-query models, you can use a single symmetric similarity model (e.g., `text-similarity-curie-001`).
An example of how to use embeddings for recommendations is shown in [Recommendations.ipynb](examples/Recommendations.ipynb). An example of how to use embeddings for recommendations is shown in [Recommendation_using_embeddings.ipynb](examples/Recommendation_using_embeddings.ipynb).
Similar to search, these cosine similarity scores can either be used on their own to rank items or as features in larger ranking algorithms. Similar to search, these cosine similarity scores can either be used on their own to rank items or as features in larger ranking algorithms.