GiftGenie : Smart Gift Recommendation System

Abstract

Gift-giving is a powerful way to strengthen relationships, but selecting the right gift often poses a challenge due to varying preferences and emotions. Gift Genie is an intelligent recommendation system designed to simplify this process by integrating emotion tracking with personalized suggestions. The system leverages emotion recognition techniques through facial expressions, sentiment analysis, and behavioral cues to understand the recipient’s current emotional state. Based on these insights, Gift Genie applies machine learning algorithms to recommend thoughtful, context-aware gifts that align with the recipient’s mood and personality. This innovative approach not only enhances user satisfaction but also bridges the emotional gap in traditional e-commerce systems by delivering personalized and meaningful gifting experiences. The project demonstrates the potential of emotion-aware technology in transforming digital gift recommendations, making them more human-centric, accurate, and impactful.

Introduction

In the modern digital era, finding the perfect gift can be difficult due to vast product choices and varying user preferences. GiftGenie is an AI-powered gift recommendation system designed to simplify this process by analyzing user preferences, occasions, and behavioral data to suggest personalized gift options.

The survey paper reviews current approaches in personalized recommendation systems, focusing on algorithmic models, data-driven techniques, and user-centric designs. It highlights key challenges such as cold-start problems, data sparsity, and personalization accuracy, while emphasizing the importance of context-aware and user-friendly designs in improving e-commerce gift selection experiences.

Proposed Methodology

The GiftGenie architecture integrates several intelligent components that interact seamlessly to deliver secure, AI-driven recommendations.

Main Components:

• User Interface: Allows users to input preferences, view suggestions, and make payments.

• Hybrid Recommendation Engine: Combines user data, product databases, and external sources to generate personalized gift ideas.

• Occasion & Context Analyzer: Interprets the occasion and emotional context.

• User Profile Manager: Manages user information and preferences.

• Feedback & Learning Module: Uses user ratings to refine future recommendations.

• Data Collection & Analysis Module: Updates gift catalog using trending data.

• Notification & Payment System: Handles alerts, order confirmations, and payment processing.

This structure ensures smooth coordination between users, AI modules, and data systems, resulting in a secure and intelligent gifting experience.

System Activity Flow

1. Start → User opens GiftGenie.

2. Login/Registration.

3. Enter Preferences → Occasion, relationship, budget, interests.

4. Data Processing → Hybrid engine and context analyzer generate recommendations.

5. Display Gift Options → Ranked personalized suggestions.

6. Select Gift & Personalize Message.

7. Add to Cart → Checkout.

8. Make Payment → Confirmation or retry if failed.

9. Order Confirmation → Notification sent; gift scheduled for dispatch.

10. User Feedback → Collected to improve system accuracy.

11. End → One complete interaction cycle.

Future Scope

1. Mobile App Development – For on-the-go access and notifications.

2. Social Media & Calendar Integration – Auto-detects special occasions for reminders.

3. Advanced Emotion Detection – Uses AI-based facial/sentiment analysis for emotional understanding.

4. Voice & Gesture Recognition – Enables natural interaction via smart assistants.

5. Enhanced Recommendation Accuracy – Continuous machine learning model training with user feedback.

Conclusion

Through modules such as the Hybrid Recommendation Engine, Occasion & Context Analyzer, and Feedback & Learning System, GiftGenie provides users with intelligent, emotion-aware, and context-based gift suggestions tailored to the recipient and occasion. The project integrates advanced technologies like machine learning, natural language processing, and secure web development to create a complete, end-to-end gifting experience — from personalized gift recommendations to message generation, gift wrapping, and payment integration. The use of encryption and secure communication ensures data privacy and builds user trust. By combining convenience, personalization, and emotional understanding, GiftGenie not only simplifies gift selection but also enhances the user’s emotional connection and satisfaction. This project demonstrates how AI can be applied to solve real-world problems creatively and effectively, transforming a simple social gesture into a smart, efficient, and meaningful digital experience.

References

[1] Huang, X., Ren, M., Han, Q., Shi, X., Nie, J., Nie, W., & Liu, A.-A. (2021). Emotion Detection for Conversations Based on Reinforcement Learning Framework. IEEE MultiMedia, 28(2), 76–85. https://doi.org/10.1109/mmul.2021.3065678 [2] Salam, S. A., & Gupta, R. (2018). Emotion Detection and Recognition from Text using Machine Learning. International Journal of Computer Sciences and Engineering, 6(6), 341–345. https://doi.org/10.26438/ijcse/v6i6.341345. [3] Zhang, R., Xie, G., Ying, J., & Hua, Z. (2025). Leveraging Large Language Models for Personalized Parkinson’s Disease Treatment. IEEE Journal of Biomedical and Health Informatics, 1–14. https://doi.org/10.1109/jbhi.2025.3594014 [4] Liu, G. (2022). Research on Personalized Minority Tourist Route Recommendation Algorithm Based on Deep Learning. Scientific Programming, 2022, 1–9. https://doi.org/10.1155/2022/8063652. [5] Research on Personalized Recommendation System of E-Commerce Based on Big Data. The Frontiers of Society, Science and Technology, 6(5). https://doi.org/10.25236/fsst.2024.060504 [6] A. Hake, “Sentiment Analysis-Based Product Review System for Enhanced Recommendations,” in Proc. IEEE Conf. Artificial Intelligence and Data Engineering (AIDE), [7] Wang, Q., & Li, J. (2024). E-commerce Recommendation Algorithm Based on K-Means Clustering. 2024 International Conference on Integrated Circuits and [8] Communication Systems (ICICACS), 1–4. https://doi.org/10.1109/icicacs60521.2024.10498325. [9] Zhu, Z. (2025). Research and Application of Personalized Recommendation Algorithm Based on Deep Learning Network. 2025 2nd International Conference on Algorithms, Software Engineering and Network Security (ASENS), 176–179. https://doi.org/10.1109/asens64990.2025.110.

Copyright

Copyright © 2025 Prof. S. Vishvabrahma, Pratiksha Solat, Supriya Sobale, Khushi Vaskar, Hanee Wararkar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.