ΕΕΛΛΑΚ - Λίστες Ταχυδρομείου

Contribution to PersonalAIs

Dear Giannis & Thanos,

I hope you are having a good day. I am Yan Zhang, a master student in IT &
Cognition at Copenhagen University. I have been closely following the
discussions around the PersonalAIs project, and I am very interested in
contributing, and it aligns closely with my background.

With my background in Music Emotion Recognition, NLP, and Full-Stack
Development, I have worked on several projects that align with the goals of
PersonalAIs:

   - Music Emotion Recognition: I conducted a comparative study on the
   performance of unimodal (audio or lyrics) and multimodal (combined audio
   and lyrics) models in Music Emotion Recognition (MER).
   - NLP & Machine Learning: I have experience in text classification,
   sentiment analysis, and author style recognition, using a machine
   model with NLP techniques.
   - Full-Stack Development: I've developed a responsive e-commerce
   platform for yoga products, featuring real-time price calculations, user
   authentication, and secure payment processing. Built RESTful APIs using
   Express.js, designed the frontend with React.js, integrated Stripe API for
   payments, and implemented MongoDB for data storage.
   - Co-Pilot and Question Answering System – GraphRAG for Workflow
   Understanding: I'm currently doing my thesis about GraphRAG and fine-tuning
   SLM models, which could be useful in exploring how PersonalAIs can leverage
   models for interactive recommendations.

By integrating these experiences, I believe I can contribute effectively to
developing a personalized music recommendation system for the PersonalAIs
project as part of GSOC 2025. I am excited about this opportunity and eager
to explore how I can contribute.

Additionally, I would like to ask about the evaluation criteria for the
project:

   - Does PersonalAIs have predefined evaluation standards? If not, do you
   think it is necessary to establish them?
   - Would you prefer to use industry-standard recommendation evaluation
   metrics (e.g., NDCG, MRR), or would a more user-feedback-driven approach be
   more suitable?

I would greatly appreciate your insights on this. Looking forward to your
feedback!

Best regards,
Yan Zhang
MSc IT & Cognition, University of Copenhagen
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