Welcome to the Attentive Recommendation Engine repository, developed during the Break Through Tech AI program! 🚀 This project showcases an advanced recommendation system using a two-tower model with attention mechanisms to deliver relevant and accurate recommendations across various applications. 💡
Our goal is to build a robust recommendation system leveraging a two-tower architecture integrated with attention mechanisms. This approach helps capture user-item interactions effectively, boosting the relevance and quality of recommendations. 🔍✨
This recommendation engine is designed to support various applications within the financial services industry, particularly enhancing customer experiences for organizations like American Express. By integrating advanced recommendation techniques, companies can:
- Improve Customer Engagement 📈: Personalize offers and recommendations, leading to higher customer satisfaction and retention.
- Boost Revenue 💰: Optimize product and service suggestions to drive up-selling and cross-selling opportunities.
- Enhance Operational Efficiency ⚙️: Automate the personalization process, reducing manual effort and increasing scalability.
- 📂 Datasets
- 🛠️ Data Cleaning and Exploratory Data Analysis (EDA)
- 🧩 Model Development
- 📈 Results and Evaluation
- 👥 Contributors
- 📜 License
This project uses publicly available datasets, including:
- User interaction data: Tracks user behavior and interactions.
- Item metadata: Contains detailed information about recommended items.
- Contextual data: Provides enriching contextual information for recommendations.
These datasets form the backbone of our training and evaluation process. 📊
Data preprocessing ensures the model's accuracy and reliability:
- Cleaning: Handle missing values, outliers, and inconsistencies.
- EDA: Explore data distributions, identify trends, and inform feature engineering. 🔍📈
Our two-tower model architecture is designed as follows:
- User Tower 🧑: Processes user features to generate embeddings.
- Item Tower 📦: Processes item features to generate embeddings.
An attention mechanism assigns importance weights to different features, allowing the model to capture complex user-item interactions more effectively. 🌟
We assess model performance using the following metrics:
- Loss 📉: Evaluates the model's error during training and testing.
- Precision 🔧: Measures the proportion of true positive recommendations.
- Accuracy ✅: Indicates the overall correctness of the model's predictions.
Baseline comparisons are conducted to demonstrate the effectiveness of our approach.
A huge shoutout to the amazing team behind this project! 🙌
Asmi K |
Paige Caskey |
Rebecca Smith |
Arda Hoke |
Tanisha Dutta |
Vaishnavi Ramanujan |
This project is licensed under the MIT License. 📝
For more details on implementation and usage, check out the individual module documentation within the repository. 🚀