From b04e3521a2d0106d1c0f791a530914fdd53a1065 Mon Sep 17 00:00:00 2001
From: Animus <54163084+animusDS@users.noreply.github.com>
Date: Fri, 31 Mar 2023 17:21:59 -0700
Subject: [PATCH] fix another grammatical error

this fixes another grammar error with assistance from @sdornan
---
 simclusters-ann/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/simclusters-ann/README.md b/simclusters-ann/README.md
index 4341555d2..b186e2c2a 100644
--- a/simclusters-ann/README.md
+++ b/simclusters-ann/README.md
@@ -12,7 +12,7 @@ The cosine similarity between two Tweet SimClusters Embedding presents the relev
 
 SimClusters from the Linear Algebra Perspective discussed the difference between the dot-product and cosine similarity in SimCluster space. We believe the cosine similarity approach is better because it avoids the bias of tweet popularity.
 
- However, calculating the cosine similarity between two Tweets is pretty expensive in Tweet candidate generation. In TWISTLY, we scan at most 15,000 (6 source tweets * 25 clusters * 100 tweets per clusters) tweet candidates for every Home Timeline request. The traditional algorithm needs to make API calls to fetch 15,000 tweet SimCluster embeddings. Considering that we need to process over 6,000 RPS, it’s hard to support by the existing infrastructure.  
+ However, calculating the cosine similarity between two Tweets is pretty expensive in Tweet candidate generation. In TWISTLY, we scan at most 15,000 (6 source tweets * 25 clusters * 100 tweets per clusters) tweet candidates for every Home Timeline request. The traditional algorithm needs to make API calls to fetch 15,000 tweet SimCluster embeddings. Considering that we need to process over 6,000 RPS, it’s hard to support with the existing infrastructure.  
 
 
 ## SimClusters Approximate Cosine Similariy Core Algorithm