{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Programming Tech Brief By HackerNoon","title":"How to Use Vector Search to Build a Movie Recommendation App","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/90d20f93\"></iframe>","width":"100%","height":180,"duration":430,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/how-to-use-vector-search-to-build-a-movie-recommendation-app.\nLearn how to build a semantic movie recommendation app using ScyllaDB’s vector search to find films by meaning, not just keywords.\nCheck more stories related to programming at: https://hackernoon.com/c/programming.\n            You can also check exclusive content about #scylladb-vector-search, #movie-recommendation-app, #semantic-search-tutorial, #vector-similarity-functions, #python-streamlit-app, #sentence-transformers, #ann-index-scylladb, #good-company,  and more.\nThis story was written by: @scylladb. Learn more about this writer by checking @scylladb's about page,\n            and for more stories, please visit hackernoon.com.\nScyllaDB’s new Vector Search lets developers build semantic search apps that understand meaning, not just text. This tutorial shows how to create a movie recommendation app using Sentence Transformers, Python, and Streamlit. It covers schema design, vector indexing, and ANN-based querying for fast, intelligent recommendations.","thumbnail_url":"https://img.transistorcdn.com/KhCapPSRkLGL2Xw8888yuChkNRWthaKapLYTvNdu4W4/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMTY2LzE2ODM1/ODIzMzAtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}