AI-Powered Recommendation Model Development
Intelligent Product Suggestions for E-commerce Growth
Unlock the power of personalization with Tecorb Technologies. We design and deploy intelligent AI/ML-based Recommendation Systems for web and mobile applications across industries like e-commerce, healthcare, OTT platforms, food tech, and more. Our models adapt in real time using cutting-edge techniques including reinforcement learning, deep learning, and hybrid filtering algorithms.
Introduction to Recommendation Systems
Recommendation systems use AI/ML algorithms to analyze user behavior, preferences, and content attributes to deliver personalized suggestions. These systems drive user engagement, retention, and conversions by helping users find relevant content or products—without having to search for them.
From "customers also bought" in e-commerce to movie suggestions on streaming apps, recommendation models are now a business-critical component of digital platforms.
Industries We Serve with Custom Recommendation Models
E-commerce
Product suggestions, upselling/cross-selling, dynamic bundles that increase cart value.
Medical
Drug recommendations, symptom-to-treatment mapping, personalized health content.
Movies/OTT
Content curation, dynamic carousels, binge watch prediction to keep users engaged.
Food & Recipes
Personalized menus, dietary suggestions, reorder prediction for food delivery apps.
Our End-to-End Recommendation System Development Process
1. Business Goal Discovery
We collaborate with your team to define KPIs like CTR, retention, or basket value, aligning the model design with business objectives.
2. Data Collection and Preprocessing
We gather structured and unstructured data from browsing behavior, purchase history, product metadata, and external APIs.
3. Feature Engineering
We design real-time and batch features including user vectors, item vectors, and context features.
4. Model Training
Depending on the use case, we implement collaborative filtering, content-based filtering, hybrid models, or reinforcement learning.
5. Deployment and Monitoring
We deploy models using FastAPI and Docker, with auto-scaling on Kubernetes and A/B testing support.
6. Continuous Feedback and Improvement
We use reinforcement learning to continuously adjust the model's policy based on user interactions.
Types of Recommendation Models We Build
Collaborative Filtering
- • User-Based: Recommends items liked by similar users
- • Item-Based: Suggests items similar to those previously engaged with
Content-Based Filtering
Uses NLP, image processing, and structured data to recommend similar items based on attributes.
Hybrid Systems
Combines both collaborative and content-based signals for better cold-start handling.
Context-Aware Recommendations
Adapts based on real-time user context like location, time of day, and device.
Advanced Techniques We Use
Natural Language Processing
For understanding item descriptions, reviews, symptoms, and search queries.
Computer Vision
Used in fashion and food to compare visual features of products or meals.
Graph Neural Networks
Map user-item relationships and explore latent similarities via embeddings.
Reinforcement Learning
For real-time optimization balancing short-term clicks with long-term customer value.
Benefits of Our Custom Recommendation Models
Increase in upsell/cross-sell
Better engagement
Reduction in churn
Get a Custom AI Recommendation Engine for Your Platform
Tecorb Technologies specializes in building custom, scalable, and high-performing AI recommendation systems tailored to your business logic. Let's help you drive more conversions, improve user satisfaction, and scale intelligently.
Interested in this project?
Pricing
Fixed Project Price
Complete project implementation
Hourly Rate
For customizations & maintenance