S. No. | Author(s) and Year | Title / Platform | Technology Used | Key Features / Limitations

1 | Goodfellow, I. et al. (2013) [1] | Challenges in Representation Learning: FER2013 Dataset | CNN, Neural Networks | Introduces FER2013 dataset for facial emotion recognition; establishes baseline for CNN-based expression classification; widely adopted benchmark.

2 | Li, S., & Deng, W. (2020) [2] | Deep Facial Expression Recognition: A Survey | CNN, RNN, Hybrid Architectures | Comprehensive overview of deep learning models for emotion recognition; compares accuracy, scalability, and real-time feasibility across architectures.

3 | Mollahosseini, A. et al. (2017) [3] | AffectNet: Database for Facial Expression Computing in the Wild | Deep CNN | Large-scale real-world emotion dataset; demonstrates real-time facial emotion detection; foundation for on-device inference applications.

4 | Park, Y., & Kahng, M. (2016) [4] | Music Recommendation Based on Emotion Using CNN | CNN, Music APIs | Uses CNN-derived facial emotion features for music recommendation; directly aligns with emotion-based music systems via streaming APIs.

5 | Sahoo, A. K. et al. (2020) [5] | Emotion-Based Movie Recommendation System | Machine Learning, Movie Databases | Develops emotion-driven movie recommendation; demonstrates how user mood influences viewing preferences; supports API-based content suggestions.

6 | Tzirakis, P. et al. (2017) [6] | End-to-End Multimodal Emotion Recognition | Deep Neural Networks, Multimodal Fusion | Combines facial, vocal, and textual cues for improved emotion accuracy; relevant for expanding beyond facial analysis to voice or chat-based cues.

7 | Adomavicius, G., & Tuzhilin, A. (2005) [7] | Toward the Next Generation of Recommender Systems | Hybrid Models, Personalization | Seminal work on personalization and contextual data in recommender systems; underpins emotion-aware recommendation logic.

8 | Haruna, K. et al. (2017) [8] | Context-Aware Recommender System: A Review | Context-Aware Systems | Reviews systems that adapt to user state and environment; treats emotion as contextual parameter for recommendations.

9 | Abadi, M. et al. (2016) [9] | TensorFlow: Large-Scale Machine Learning | TensorFlow Framework | Introduces TensorFlow framework; enables scalability, portability, and efficient TensorFlow Lite deployment for mobile applications.

10 | Howard, A. G. et al. (2017) [10] | MobileNets: Efficient CNNs for Mobile Vision | MobileNet, TensorFlow Lite | Proposes lightweight CNN architectures for mobile and embedded systems; enables efficient on-device emotion recognition deployment.