3 Ways to Build Song Recommendation Engines

The blog post describes the development of a web app, FairytaleDJ, which recommends Disney songs based on user input by leveraging technologies such as LangChain, OpenAI's ChatGPT, Deep Lake, and Streamlit. The app uses a novel approach by encoding both user inputs and song lyrics into emotional representations, allowing for more accurate song recommendations compared to traditional methods of direct embedding similarity searches, which often fail to capture the nuanced match between user emotions and song themes. The development process involved scraping Disney song lyrics, using Spotify APIs to access song embeddings, and employing Deep Lake to store these embeddings. The team experimented with various methods, ultimately finding success with emotion-based embeddings, which provided qualitatively better results and allowed for efficient retrieval through a vector database. The app is hosted on Hugging Face, demonstrating the practical application of these technologies in creating personalized music recommendations, and highlights the importance of choosing the right technology to build effective recommendation engines.

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