Dear Aditya,
Thank you for reaching out and for sharing your structured plan for
the *PersonalAIs:
Generative AI Agent for Personalized Music Recommendations* project! Your
experience in *GenAI, NLP, sentiment analysis, and backend development* is
highly relevant, and we appreciate the thought you’ve put into designing a
detailed *flow implementation*.
*Feedback on Your Approach*
Your outlined approach aligns well with the project objectives, and here
are some key aspects we’d like to highlight:
1.
*User Authentication & Data Retrieval*
- Using *Spotify OAuth* for authentication and fetching user listening
data is an essential part of the project.
- Since some Spotify API endpoints (e.g., *Get Recommendations, Get
Track's Audio Analysis*) have been deprecated, alternative approaches
(e.g., *Essentia for audio analysis*) may need to be explored.
2.
*NLP-Based Mood & Sentiment Analysis*
- Your plan to use *Deepset R1, BERT, Hugging Face models, and VADER* is
solid.
- Consider testing how *real-time sentiment shifts* impact playlist
adjustments dynamically.
3.
*Generative AI for Conversational Interaction*
- Using *Deepset R1* for query interpretation is a great approach.
- Ensure the system can distinguish *direct playlist requests*
from *general
conversational input* to avoid unnecessary playlist generation.
4.
*Hybrid Recommendation System*
- *Collaborative & content-based filtering* combined with *FAISS-based
vector similarity search* is a well-rounded approach.
- *Reinforcement learning* for optimizing recommendations over time
is an exciting direction—consider how user feedback (likes, skips, mood
shifts) can be incorporated effectively.
5.
*Real-Time Conversational Modifications*
- Handling commands like *“make it more chill”* is a key feature of the
project.
- Ensure the *re-ranking mechanism* is efficient to avoid unnecessary
re-fetching of large datasets.
6.
*Backend & Frontend Implementation*
- *FastAPI or Express.js* will work well for backend services handling
API calls and AI model execution.
- *React.js* for a chatbot-style UI is a great choice; *Streamlit*
could be useful for quick prototyping but may not be ideal for
production.
- Consider how session state will be managed for smoother user
interactions.
*Next Steps*
- Continue *experimenting with the Spotify API*, ensuring that the
planned features align with its latest capabilities.
- Explore *mood-based filtering alternatives* in case direct audio
analysis endpoints are not available.
- If you have an early prototype (even a small proof-of-concept), feel
free to share it for feedback.
Please include everything in a draft proposal.
Let us know if you need any additional guidance, and we look forward to
reviewing your proposal!
Best regards,
Giannis Prokopiou & Thanos Aidinis
Στις Τετ 12 Μαρ 2025 στις 7:56 μ.μ., ο/η Aditya <adityabhaskar201 [ at ] gmail [ dot ] com>
έγραψε:
> γεια ,
> I hope this message finds you all well , I am writing this message for
> regarding Google Summer of Code for organisation Open technology alliance -
> GFOSS "ellark" . I want to contribute in project "PersonalAIs: Generative
> AI Agent for Personalized Music Recommendations
> " because I have good experience in GenAI technologies which I gained my
> internship at Airport Authority of India as Machine Learning engineer . I
> know how to build a Retrieval augment generation model which included use
> of llm , transformers,embedding , etc . I have good experience with nlp and
> sentiment analysis which we can use in building personalised music
> recommendation AI agent .
> I have experience with backend technologies also , so integrating Spotify
> api wont be any trouble .
> Here is what I have planned ,
> I have attached an image so that we can get quick idea .
>
>
>
> Flow Implementation for PersonalAIs
> 1. User authentication and data retrieval
> • User logs in via Spotify API OAuth
> • Fetch user data including liked songs, playlists, and listening history
> • Extract audio features such as tempo, energy, mood, and danceability
> 2. NLP based mood and sentiment analysis
> • Process user text input with Deepset R1, BERT, Huggingface, and VADER
> • Identify mood, sentiment, and emotional context
> 3. Generative AI for conversations
> • Use Deepset R1 on Huggingface for chatbot like interaction
> • Interpret queries like give me something energetic
> • Adjust recommendations based on real time feedback
> 4. Personalized music recommendation system
> • Hybrid filtering approach
> o Collaborative filtering suggests songs based on similar users
> o Content based filtering matches tracks to user preferences
> o FAISS vector similarity search finds mood related song embeddings
> o Reinforcement learning improves recommendations over time
> 5. Real time conversational modifications
> • User modifies preferences like make it more chill
> • AI dynamically re filters playlist with adjusted energy, mood, and genre
> 6. Backend processing
> • FastAPI in Python or Express.js in Node.js handles
> o AI model execution
> o Spotify API requests
> o User session and playlist management
> 7. Frontend UI for user interaction
> • React.js or Streamlit for a chatbot style interface
> • Real time playlist updates and feedback from AI
> 8. Data storage and deployment
> • Store user preferences and past interactions in PostgreSQL or MongoDB
> • Deploy on local server or AWS for scalability
>
> Feel free to contact me anytime , If more details needed .
> Thanks !
> ----
> Λαμβάνετε αυτό το μήνυμα απο την λίστα: Λίστα αλληλογραφίας και συζητήσεων
> που απευθύνεται σε φοιτητές developers \& mentors έργων του Google Summer
> of Code - A discussion list for student developers and mentors of Google
> Summer of Code projects.,
> https://lists.ellak.gr/gsoc-developers/listinfo.html
> Μπορείτε να απεγγραφείτε από τη λίστα στέλνοντας κενό μήνυμα ηλ.
> ταχυδρομείου στη διεύθυνση <gsoc-developers+unsubscribe [ at ] ellak [ dot ] gr>.
>
----
Λαμβάνετε αυτό το μήνυμα απο την λίστα: Λίστα αλληλογραφίας και συζητήσεων που απευθύνεται σε φοιτητές developers \& mentors έργων του Google Summer of Code - A discussion list for student developers and mentors of Google Summer of Code projects.,
https://lists.ellak.gr/gsoc-developers/listinfo.html
Μπορείτε να απεγγραφείτε από τη λίστα στέλνοντας κενό μήνυμα ηλ. ταχυδρομείου στη διεύθυνση <gsoc-developers+unsubscribe [ at ] ellak [ dot ] gr>.