From 27ed4dc8c96798ebcfa23c125b3116c16a1023e7 Mon Sep 17 00:00:00 2001 From: Mike Heaton Date: Thu, 9 Jun 2022 14:28:45 -0700 Subject: [PATCH] fix link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ca87cacf..85258463 100644 --- a/README.md +++ b/README.md @@ -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`). -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.