Enhanced Content Recommendation System for Niche Streaming Platform

Medium Priority
AI & Machine Learning
Streaming Platforms
👁️7283 views
💬550 quotes
$25k - $75k
Timeline: 12-16 weeks

Our SME streaming platform is seeking to develop a cutting-edge AI-powered content recommendation system. Utilizing advanced machine learning algorithms, this system aims to enhance user engagement by providing personalized content suggestions. We are targeting a smarter, more intuitive user experience that keeps viewers engaged longer and reduces churn.

📋Project Details

Our niche streaming platform caters to a dedicated audience with unique content preferences. To enhance user engagement and retention, we aim to develop a sophisticated content recommendation system leveraging the latest advancements in AI and machine learning. By utilizing technologies such as OpenAI API, TensorFlow, and PyTorch, we plan to build a robust solution capable of analyzing user data to predict and recommend content tailored to individual tastes. The project will involve integrating Natural Language Processing (NLP) to understand viewing habits and preferences better, while also employing predictive analytics to anticipate future trends. The ultimate goal is to create a system that not only enhances user satisfaction but also increases content consumption, thereby driving up revenue. This project will be developed over 12-16 weeks, with a budget ranging from $25,000 to $75,000.

Requirements

  • Integration with existing platform
  • User data analysis and processing
  • Development of personalized recommendation algorithms

🛠️Skills Required

Machine Learning
NLP
TensorFlow
PyTorch
OpenAI API

📊Business Analysis

🎯Target Audience

Our target audience consists of niche content consumers who value personalized and curated viewing experiences. They are typically tech-savvy, appreciate detailed recommendations, and engage deeply with platform content.

⚠️Problem Statement

Our current content recommendation system is inadequate in engaging users and increasing watch time. Without personalization, users often get irrelevant suggestions which results in higher churn rates.

💰Payment Readiness

The market readiness to pay for enhanced recommendation systems is driven by the need for better user engagement, which is crucial for maintaining competitive advantage and boosting subscription renewals.

🚨Consequences

Without an improved recommendation system, we risk losing market share to competitors who offer superior, personalized viewing experiences. This could result in decreased user engagement and potential revenue loss.

🔍Market Alternatives

Currently, competitors are employing basic algorithmic recommendations which do not utilize advanced AI insights. Our strategy is to surpass these by integrating AI-driven personalization.

Unique Selling Proposition

Our system will offer a unique blend of AI-driven personalization with real-time user engagement metrics, setting us apart from competitors relying on conventional recommendation algorithms.

📈Customer Acquisition Strategy

Our go-to-market strategy involves targeting our existing user base with tailored marketing campaigns highlighting enhanced personalization. Additionally, collaboration with tech influencers to showcase our platform's new capabilities will aid in broadening our reach.

Project Stats

Posted:August 6, 2025
Budget:$25,000 - $75,000
Timeline:12-16 weeks
Priority:Medium Priority
👁️Views:7283
💬Quotes:550

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