Personalized AI/ML Recommendation System Development
Budget: ₹600 – ₹1,500 INR
**Project Title:**
Personalized Recommendation System Using Collaborative Filtering and Deep Learning
**Project Description:**
I am looking for an experienced AI,ML,DL developer to build a personalized recommendation system using collaborative filtering and deep learning techniques. The system should analyze user–item interaction data and generate personalized recommendations for users.
**Project Objectives:**
* Develop a recommendation model using collaborative filtering.
* Integrate deep learning techniques (such as Neural Collaborative Filtering).
* Improve recommendation accuracy by capturing complex user–item relationships.
* Generate Top-N personalized recommendations.
**Scope of Work:**
1. Data preprocessing and creation of a user–item interaction matrix.
2. Implementation of collaborative filtering methods.
3. Development of a deep learning model for recommendation (e.g., neural network–based recommender).
4. Model training and evaluation using appropriate metrics.
5. Comparison with traditional recommendation approaches.
6. Visualization of results and performance metrics.
**Expected Deliverables:**
* Fully working Python implementation.
* Well-structured code with comments.
* Documentation explaining the methodology and architecture.
* Evaluation results using metrics such as RMSE, Precision, Recall, or F1-score.
* A short report explaining the system design and results.
Dataset you have to find on your own
Project Type:
Academic / Research Project
Personalized Recommendation System Using Collaborative Filtering and Deep Learning
**Project Description:**
I am looking for an experienced AI,ML,DL developer to build a personalized recommendation system using collaborative filtering and deep learning techniques. The system should analyze user–item interaction data and generate personalized recommendations for users.
**Project Objectives:**
* Develop a recommendation model using collaborative filtering.
* Integrate deep learning techniques (such as Neural Collaborative Filtering).
* Improve recommendation accuracy by capturing complex user–item relationships.
* Generate Top-N personalized recommendations.
**Scope of Work:**
1. Data preprocessing and creation of a user–item interaction matrix.
2. Implementation of collaborative filtering methods.
3. Development of a deep learning model for recommendation (e.g., neural network–based recommender).
4. Model training and evaluation using appropriate metrics.
5. Comparison with traditional recommendation approaches.
6. Visualization of results and performance metrics.
**Expected Deliverables:**
* Fully working Python implementation.
* Well-structured code with comments.
* Documentation explaining the methodology and architecture.
* Evaluation results using metrics such as RMSE, Precision, Recall, or F1-score.
* A short report explaining the system design and results.
Dataset you have to find on your own
Project Type:
Academic / Research Project