4. Music Recommendation#
4.1. Overview#
By leveraging their natural language understanding and generation capabilities, LLMs can assist in creating rich, human-readable descriptions of songs, artists, and genres based on their audio features, metadata, and other contextual information.
Here’s how it could work:
Finetune an LLM on a dataset of song descriptions, artist bios, genre overviews, and other music-related text data.
For a given song, extract its audio features (e.g., tempo, key, instrumentation, audio embeddings) and obtain metadata (e.g., artist, genre, release year).
Use the LLM to generate a natural language description of the song, incorporating the extracted information and drawing upon its learned understanding of music discourse.
Match these generated descriptions with user preferences or profiles expressed in natural language (e.g., “I like upbeat pop songs with catchy melodies” or “Recommend something similar to Radiohead’s experimental rock sound”).
The key advantage of this approach is that it can provide more intuitive and human-friendly music recommendations compared to traditional collaborative filtering or content-based methods. By operating in the natural language domain, LLMs can better capture the nuances and subjective qualities that humans associate with music, leading to more personalized and satisfying recommendations.
4.2. Limitation#
However, there are also limitations and potential biases to consider:
Popularity Bias: LLMs may tend to generate descriptions that align with popular or mainstream music, potentially overlooking niche or lesser-known artists or genres.
Subjectivity: Music appreciation is highly subjective, and LLM-generated descriptions may not always resonate with individual users’ preferences or interpretations.
Context Limitations: LLMs may struggle to capture contextual factors that influence music preferences, such as mood, activity, or social setting.
To mitigate these limitations and create a truly useful music recommendation system, LLM-generated descriptions would need to be combined with a robust music retrieval engine and personalization algorithms. Additionally, incorporating user feedback and continuously fine-tuning the LLM on diverse music data could help reduce biases and improve the quality of recommendations over time.