Hybrid AI for recommender systems

Uses both product attributes and user behavior for deeper personalization

Instantly adapts to changing catalogs

Makes accurate suggestions even with limited user or item data

Matches users with content that fits their evolving interests

Outperforms traditional recommendation models

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.

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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.

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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.