update readme/colab link

Author: rbs333

README.md

@@ -94,6 +94,11 @@ For further insights on enhancing RAG applications with dense content representa
 
 ## Recommendation systems
 
+| Recipe | Description |
+| --- | --- |
+| [/recommendation-systems/content_filtering.ipynb](python-recipes/recommendation-systems/content_filtering.ipynb) | Intro content filtering example with redisvl |
+
+### See also
 An exciting example of how Redis can power production-ready systems is highlighted in our collaboration with [NVIDIA](https://developer.nvidia.com/blog/offline-to-online-feature-storage-for-real-time-recommendation-systems-with-nvidia-merlin/) to construct a state-of-the-art recommendation system.
 
 Within [this repository](https://github.com/redis-developer/redis-nvidia-recsys), you'll find three examples, each escalating in complexity, showcasing the process of building such a system.

python-recipes/recommender-systems/content_filtering.ipynb renamed to python-recipes/recommendation-systems/content_filtering.ipynb

@@ -11,7 +11,7 @@
     "In this recipe you'll learn how to build a content filtering recommender system (RecSys) from scratch using RedisVl and an IMDB movies dataset.\n",
     "\n",
     "## Let's Begin!\n",
-    "<a href=\"https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/01_redisvl.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/recomendation-systems/content_filtering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
    ]
   },
   {