{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/bf19084106064101a984bf4645830a2e\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/bf19084106064101a984bf4645830a2e-fff2f1bb7eddd4bb.gif","duration":219.427,"title":"Music Recommender User Profiles and Guardrails","description":"I added a user profile generator to my music recommender. Using a Streamlit front end, users click through a list of songs and mark which they like or dislike, and the system generates a profile using heuristics like pop, high energy, and happy, then produces top recommendations based on that. I also demonstrated terminal guardrails to sanity check the backend outputs, like making sure recommendations are in descending order and genre constraints are respected. Next, I want to build a time gated profile using your last seven weeks of listens, and weight recent activity at about 60 percent. No action is requested from viewers."}