AMRAI (Advanced Music Recommendation AI)
As a music lover, I am excited to create an AI tool that can greatly enhance the music streaming experience.
MAIN THESES:
- It is important to have music associations contained in data for the following model training. For instance, if someone likes one artist and often adds to the playlist music of another, it is more likely that the other person would also love the music of the second artist. Developing and learning this deep "nearly-subconscious" connections are some key features of AI that I'm planning to create.
- Every person's music taste is unique, as well as their experience and emotions while listening to it. Most music compositions are deeply connected to our memories, such as places, people, and events. That is why sharing your emotions in addition to the data would contribute even more.
- For every song, the following characteristics will be provided: acousticness / danceability / duration / energy / instrumentalness / key / liveness / loudness / mode / speechiness / tempo / time signature / valence / target / songtitle / artist. These dataframes will be used as AI "navigation" system.
- A list of Music related Datasets will be used to gather and process the data needed, with respect to privacy settings, as well as Terms and Conditions of Use.
- To truly understand and reflect individual taste, the system must learn from a user's past listening history, rating system (like/dislike), and feedback, creating a dynamic and evolving model that gets smarter over time. It could include the types of songs listened to at different times of day, days of the week, or during different activities.
- To provide a holistic user experience, the system can also incorporate an intelligent mood detection system. This can be done by analysing the user's daily activities, weather, or even text sentiment from their social media posts (if the user permits). This information can be used to recommend music that suits the user's current mood or situation.
- It is equally important to acknowledge and consider the cultural, regional, and linguistic preferences of the user. The system should be adept at recognizing such patterns to make meaningful recommendations. The system must also allow users to explore music outside their regular taste, expanding their musical horizons.
I may expand and edit main theses list. It's my first steps towards creating something unique, I greatly appreciate your understanding.
Pinecone Implementation:
- Lyrics Preprocessing: Cleaning the lyrics data. This might involve removing stop words (like "and", "the", "a"), punctuation, and other unnecessary elements. Use of stemming or lemmatization to reduce words to their base or root form.
- Lyrics Vector Embedding: Transforming preprocessed lyrics into vector embeddings. This will create a numerical representation of each song's lyrics that will be used in a future model.
- Pinecone Indexing: Will connect song features and lyrics vectors of the same dimension, or by creating separate indices for each.
- Model Training: pretty much routine process:)
Therefore, I invite you to actively participate in this project. To do this, you can start by sharing your music tastes in the discussions below. Feel free to comment on what genres, artists, or particular songs you enjoy. I also would love to hear any thoughts or suggestions you might have regarding AMRAI. If you're willing, I'd appreciate it if you could test the system once it's ready and provide feedback on its performance. Lastly, it would be a great help if you could invite your friends to participate as well, as the more diverse our user base, the better our AI will become. Please don't hesitate to ask any questions regarding this project.
Thank you in advance, I truly value your contribution!