• S Sameer
• Syead MD Abrar
• Virupakshi H
• Shashavali P
• DR. MANJULA S D

Modern consumer systems require advanced personalized solutions to improve user experience and reduce decision fatigue. This paper presents an Adaptive Mood-Based Food and Music Recommender that integrates real-time facial emotion recognition (FER), authenticated user profiles, and an adaptive feedback loop. The system uses a convolutional neural network (CNN) model to determine the user's mood, which then triggers personalized food and music suggestions. Key features include user authentication, first-time user onboarding to collect initial preferences, and a persistent feedback mechanism that filters out disliked items across sessions. Testing showed reliable performance in mood detection, secure access, and delivering relevant, adaptive recommendations, offering a multifunctional solution for next-generation personalized platforms.

Neutral , Happy , Sad , Surprise , Recommender System , Mood Detection , Computer Vision , OpenCV, User Feedback Loop , Adaptive Filtering , Food Recommendation Engine , Facial Emotion Recognition (FER), Convolutional Neural Network (CNN) , Real-Time Processing.

"Food Recommendation Based on Mood", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 11, page no.c647-c650, November-2025, Available :http://www.jetir.org/papers/JETIR2511279.pdf

"Food Recommendation Based on Mood", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 11, page no. ppc647-c650, November-2025, Available at : http://www.jetir.org/papers/JETIR2511279.pdf