Your AI engine behind smarter recommendations
Boost customer engagement with personalized, context-aware recommendations driven by a hybrid Knowledge Graph Attention Network (KGAT).
Book demoHow to use
Step 1
Feed your data into the model
Prepare your existing user and product data, like interactions, attributes, demographics, and input it into the KGAT system.
Step 2
Generate personalized recommendations
Run the model to get tailored suggestions that match your customers and products.
Step 3
Update your data regularly
Keep feeding new user interactions and product updates into the system.
Fuel your recommendations with the full picture
Provide recommendations that use customer demographics, behavior, and social links along with item data to make results sharper and more targeted.
Combine item-side and user-side data
Deploy a hybrid AI model that merges product attributes and user behavior for deeper personalization.
Process sparse data with ease
Keep your recommendations meaningful and engaging, even for new users or items with little interaction history.
Adapt to dynamic product catalogs
Handle new users and items on the fly without retraining from the ground up. Great for businesses with frequently changing catalogs.
Focus on the most relevant information
With a knowledge-aware attention mechanism, your recommender knows which connections (items, attributes) matter most to each user's taste.
Reduce irrelevant suggestions
Deliver more accurate suggestions with a hybrid KGAT-based system that distinguishes between positive and negative ratings.
Scale with growing data
Expand effortlessly as your user base, product set, and feature space grow.
Enhance your recommendation system in 3 steps
Why build complex data networks from scratch?
Streamline the recommendation process with our hybrid KGAT model. Here is how to get started.
Feed your data into the model
Prepare your existing user and product data, like interactions, attributes, demographics, and input it into the KGAT system.
Generate personalized recommendations
Run the model to get tailored suggestions that match your customers and products.
Update your data regularly
Keep feeding new user interactions and product updates into the system.
Customizations we provide
Integrate with your specific data sources, like CRM databases, e-commerce platforms, web analytics, and social media feeds.
Define and weight different interaction types (views, clicks, purchases, ratings) according to their impact.
Adjust model hyperparameters like embedding size, attention heads, and learning rates for your dataset.
Implement real-time or frequent data updates to keep recommendations fresh.
Built for businesses where every click, view, or booking counts
Whether you're selling sneakers, streaming content, or offering courses, our hybrid KGAT-based model delivers spot-on recommendations that make users scroll and buy.
E-commerce marketplaces
Recommend products that match individual tastes, trends, and purchase history, even for first-time visitors or new listings.
Content streaming platforms
Drive user engagement with a recommender that understands what viewers love. And what they're likely to love next.
Travel & booking platforms
Show relevant destinations, accommodations, or experiences based on user intent, past bookings, and seasonality.
Online learning platforms
Suggest courses or learning materials tailored to users' backgrounds, interests, and progress for improved learning outcomes.
Win customer attention and keep it
Drive deeper engagement
Offer relevant recommendations that encourage users to return and stay connected over time.
Accelerate conversions
Boost click-through rates and purchases with context-aware suggestions.
Simplify product discovery
Help users uncover niche or new items they might have missed.
Enhance accuracy
Generate better-targeted recommendations with AI that outperforms traditional models.
Recommend smarter with hybrid AI
Learn how our hybrid KGAT model makes your recommendations more personalized and accurate.
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