From 41afee9614e63c17e1a875a2ed2f2a550c1b7266 Mon Sep 17 00:00:00 2001 From: navanchauhan Date: Sun, 22 May 2022 12:30:17 -0600 Subject: fixed for twitter thread --- docs/posts/2022-05-21-Similar-Movies-Recommender.html | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) (limited to 'docs/posts/2022-05-21-Similar-Movies-Recommender.html') diff --git a/docs/posts/2022-05-21-Similar-Movies-Recommender.html b/docs/posts/2022-05-21-Similar-Movies-Recommender.html index 2c0b488..2e2fb6b 100644 --- a/docs/posts/2022-05-21-Similar-Movies-Recommender.html +++ b/docs/posts/2022-05-21-Similar-Movies-Recommender.html @@ -240,7 +240,9 @@

Because of the small size of the database file, I was able to just upload the file.

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For the encoding model, I decided to use the pretrained paraphrase-multilingual-MiniLM-L12-v2 model for SentenceTransformers, a Python framework for SOTA sentence, text and image embeddings. I wanted to use a multilingual model as I personally consume content in various languages (natively, no dubs or subs) and some of the sources for their information do not translate to English. As of writing this post, I did not include any other database except Trakt.

+

For the encoding model, I decided to use the pretrained paraphrase-multilingual-MiniLM-L12-v2 model for SentenceTransformers, a Python framework for SOTA sentence, text and image embeddings. +I wanted to use a multilingual model as I personally consume content in various languages and some of the sources for their information do not translate to English. +As of writing this post, I did not include any other database except Trakt.

While deciding how I was going to process the embeddings, I came across multiple solutions:

@@ -299,7 +301,8 @@

We use the trakt_id for the movie as the ID for the vectors and upsert it into the index.

-

To find similar items, we will first have to map the name of the movie to its trakt_id, get the embeddings we have for that id and then perform a similarity search. It is possible that this additional step of mapping could be avoided by storing information as metadata in the index.

+

To find similar items, we will first have to map the name of the movie to its trakt_id, get the embeddings we have for that id and then perform a similarity search. +It is possible that this additional step of mapping could be avoided by storing information as metadata in the index.

def get_trakt_id(df, title: str):
   rec = df[df["title"].str.lower()==movie_name.lower()]
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