Digify: Personalized Content Recommendation Platform Using Scrum

Introduction

Digify aims to enhance user engagement and satisfaction by developing a personalized content recommendation platform. This platform will leverage user data and advanced machine learning algorithms to provide tailored content suggestions, thereby improving the overall user experience and increasing user retention.

Vision

Our vision is to create a state-of-the-art recommendation system that understands user preferences and delivers highly relevant content. This platform will be a key differentiator for Digify in the competitive streaming service market, driving both user satisfaction and business growth.

Goals

• Increase User Engagement: By providing personalized content recommendations, we aim to increase the average time users spend on our platform.
• Improve User Retention: Personalized experiences will encourage users to continue their subscriptions and reduce churn rates.
• Enhance User Satisfaction: Delivering content that aligns with user preferences will improve overall satisfaction and loyalty.
• Leverage Data Insights: Utilize user data to gain insights into viewing habits and preferences, guiding content acquisition and marketing strategies.

Team Roles and Responsibilities

Product Owner (PO): Sophia Martinez

• Defines the vision of the product.
• Manages the product backlog
• Engages with stakeholders to gather requirements and feedback.

Scrum Master (SM): David Lee

• Facilitates scrum ceremonies.
• Ensures the team adheres to scrum principles.
• Removes impediments to the team’s progress.

Development Team:

• Backend Developer: Alex Johnson Tech Stack: Python, Django, PostgreSQL, Redis
• Frontend Developer: Maya Patel Tech Stack: React, Redux, Tailwind CSS
• Data Scientist/ML Engineer: Chris Nguyen Tech Stack: TensorFlow, PyTorch, Scikit-Learn
• DevOps Engineer: Emma Davis Tech Stack: Docker, Kubernetes, AWS

Project Phases and Sprints

Development Phases

1. Initiation
2. Planning
3. Execution
4. Monitoring and Control
5. Closure

Sprint Planning

• Sprint Duration: 2 weeks
• Total Sprints: 8 (16 weeks total development time)

Sprint Breakdown

Sprint 1: Project Initiation and Setup

Objectives:

• Set up the development environment.
• Define project scope and requirements.
• Initial project kickoff meeting.

Tasks:

• Configure development tools and repositories (Alex, Maya, Emma).
• Create a detailed project roadmap (Sophia).
• Initial data collection framework setup (Chris).

Sprint 2: Data Collection and Content Metadata

Objectives:

• Implement user data collection mechanisms.
• Gather and store content metadata.

Tasks:

• Develop API for user data collection (Alex).
• Set up the database schema for content metadata (Alex).
• Integrate content metadata from external sources (Chris).

Sprint 3: Basic Frontend and Backend Integration

Objectives:

• Develop initial frontend interface.
• Integrate frontend with backend APIs.

Tasks:

• Create basic UI components (Maya).
• Develop API endpoints for frontend integration (Alex).
• Connect frontend to backend APIs (Maya).

Sprint 4: Collaborative Filtering Recommendation Engine

Objectives:

• Develop user-based and item-based collaborative filtering algorithms.

Tasks:

• Implement collaborative filtering algorithms (Chris).
• Test and validate algorithm performance (Chris).
• Integrate recommendation engine with backend (Alex).

Sprint 5: Content-Based Filtering Recommendation Engine

Objectives:

• Develop content-based filtering algorithms using TF-IDF.

Tasks:

• Implement content-based filtering algorithms (Chris).
• Test and validate algorithm performance (Chris).
• Integrate content-based filtering with backend (Alex).

Sprint 6: Hybrid Recommendation System

Objectives:

• Combine collaborative and content-based methods for improved accuracy.

Tasks:

• Develop hybrid recommendation algorithms (Chris).
• Validate and test hybrid model (Chris).
• Integration with backend and fine-tuning (Alex).

Sprint 7: User Interface Enhancements and Personalization

Objectives:

• Enhance UI/UX based on user feedback.
• Implement user personalization features.

Tasks:

• Refine UI components and user flow (Maya).
• Add personalization features (Maya).
• Conduct usability testing (David).

Sprint 8: Deployment and Monitoring

Objectives:

• Deploy the platform on AWS.
• Set up monitoring and logging.

Tasks:

• Containerize application using Docker (Emma).
• Deploy on AWS using Kubernetes (Emma).
• Implement monitoring and logging tools (Emma

Βudget

Estimated Budget: $250,000

Development Costs:

• Salaries: $150,000
• Tools and Software: $20,000
• Cloud Services (AWS, Databases): $30,000

Miscellaneous Costs:

• Contingency Fund: $20,000
• Marketing and User Testing: $30,000

Profit Projection

Initial Investment: $250,000

Year 1:

• Revenue: $500,000 (Increased subscriptions and user engagement)
• Profit: $250,000

Year 2:

• Revenue: $750,000
• Profit: $500,000

Year 3:

• Revenue: $1,000,000
• Profit: $750,000

Resource Allocation

Development Tools :GitHub, Jira, Slack, AWS, Docker, Kubernetes.

Team:

• Backend Developer: Alex Johnson (Full-time)
• Frontend Developer: Maya Patel (Full-time)
• Data Scientist/ML Engineer: Chris Nguyen (Full-time)
• DevOps Engineer: Emma Davis (Part-time)
• Hardware: Development machines, cloud infrastructure.

Risks and Mitigation

Data Privacy Concerns:

• Risk: User data privacy issues could lead to legal and reputational damage.
• Mitigation: Implement strict data security measures, comply with GDPR, conduct regular audits.

Algorithm Performance:

• Risk: Poor algorithm performance may lead to inaccurate recommendations, affecting user experience.
• Mitigation: Continuous testing and validation, A/B testing with real users, iterative improvements.

Scalability Issues:

• Risk: Platform may face performance issues as user base grows.
• Mitigation: Use scalable cloud services, implement microservices architecture, regular performance testing.

Budget Overrun:

• Risk: Project may exceed the allocated budget.
• Mitigation: Regular budget reviews, strict financial controls, maintain contingency fund.

Team Productivity:

• Risk: Team may face productivity issues due to various reasons (e.g., burnout, miscommunication).
• Mitigation: Effective scrum practices, regular team-building activities, provide adequate support and resources.

Summary

This structured approach outlines the development of Digify's personalized content recommendation platform using Scrum methodology. By breaking the project into manageable sprints, defining clear roles and responsibilities, and allocating resources effectively, we aim to deliver a high-quality product on time and within budget, ultimately driving business growth and enhancing user satisfaction.