From 4054e8f7c04c93b07ca2e8e0910ce136858f3402 Mon Sep 17 00:00:00 2001 From: navanchauhan <navanchauhan@gmail.com> Date: Sun, 22 May 2022 12:30:17 -0600 Subject: fixed for twitter thread --- Content/posts/2022-05-21-Similar-Movies-Recommender.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) (limited to 'Content/posts') diff --git a/Content/posts/2022-05-21-Similar-Movies-Recommender.md b/Content/posts/2022-05-21-Similar-Movies-Recommender.md index b889002..66dd54a 100644 --- a/Content/posts/2022-05-21-Similar-Movies-Recommender.md +++ b/Content/posts/2022-05-21-Similar-Movies-Recommender.md @@ -208,7 +208,9 @@ I did not want to put my poor Mac through the estimated 23 hours it would have t Because of the small size of the database file, I was able to just upload the file. -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: @@ -269,7 +271,8 @@ That's it! 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. ```python def get_trakt_id(df, title: str): -- cgit v1.2.3