From 3e56bcf356da25b0267dff84e7cfc33fe7852b32 Mon Sep 17 00:00:00 2001 From: davidmyriel Date: Tue, 25 Jun 2024 16:14:59 -0700 Subject: [PATCH] fix notebook link --- .../documentation/tutorials/collaborative-filtering.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/qdrant-landing/content/documentation/tutorials/collaborative-filtering.md b/qdrant-landing/content/documentation/tutorials/collaborative-filtering.md index 9d2a6f14c..809369fa1 100644 --- a/qdrant-landing/content/documentation/tutorials/collaborative-filtering.md +++ b/qdrant-landing/content/documentation/tutorials/collaborative-filtering.md @@ -9,7 +9,7 @@ weight: 23 # Create a collaborative filtering system -| Time: 45 min | Level: Intermediate | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://githubtocolab.com/infoslack/qdrant-example/blob/main/sparse-vectors/collaborative-filtering.ipynb) | | +| Time: 45 min | Level: Intermediate | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://githubtocolab.com/qdrant/examples/blob/master/collaborative-filtering/collaborative-filtering.ipynb) | | |--------------|---------------------|--|----| Every time Spotify recommends the next song from a band you've never heard of, it uses a recommendation algorithm based on other users' interactions with that song. This type of algorithm is known as **collaborative filtering**. @@ -28,7 +28,7 @@ Fortunately, there is a way to build collaborative filtering systems without any To implement this, you will use a simple yet powerful resource: [Qdrant with Sparse Vectors](https://qdrant.tech/articles/sparse-vectors/). -Notebook: [You can try this code here](https://github.com/infoslack/qdrant-example/blob/main/sparse-vectors/collaborative-filtering.ipynb) +Notebook: [You can try this code here](https://githubtocolab.com/qdrant/examples/blob/master/collaborative-filtering/collaborative-filtering.ipynb) ### Setup @@ -247,7 +247,7 @@ display(HTML(html_content)) ``` ## Recommendations -For a complete display of movie posters, check the [notebook output](https://github.com/infoslack/qdrant-example/blob/main/sparse-vectors/collaborative-filtering.ipynb). Here are the results without html content. +For a complete display of movie posters, check the [notebook output](https://github.com/qdrant/examples/blob/master/collaborative-filtering/collaborative-filtering.ipynb). Here are the results without html content. ```bash Toy Story, Score: 131.2033799