Recommendation engines

can help marketers and organizations increase the likelihood of arriving at recommendations tailored to a user’s past online activity or behavior using in-depth knowledge based on big data analysis.


is a powerful product recommendations engine that uses machine-learning to select the most relevant, data-driven personalized product recommendations for each customer interaction.

Homa uses machine learning to understand and predict human behavior in a helpful way. recommendation engine is used in real-time to deliver highly relevant suggestion to micro-segments of consumers.

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User behavior analytics

Gleaning and analyzing user behavior patterns from “high-information users” (highly engaged users), such as the number of times the user clicks the picture of a particular make of a vehicle, the amount of time the user spends on the product information page, etc.

Web Crawling

Graphbeen will can crawl websites while actively looking for a set of keywords that would trigger a specified action. This keywords can determine the category, specs, facts, tags or etc.

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web crawling setup can do the job if it’s programmed to fetch content from a website that has a set of keywords which will be analyzed and matched in tree of knowledge


Machine Learning BasedElimination of the guesswork of product recommendations
Real-time RecommendationsRelevant recommendations since the consumer will still be in the market for the product or service
Continuous Machine LearningA smarter system that updates and improves recommendations increasingly over time
Scalability to millions of products and consumersIncreased efficiency of marketing campaigns and cost savings