Graphbeen
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.
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.
This filtering method is usually based on collecting and analyzing information on user’s behaviors, their past behavior, as well as similar historical activities by other people and predicting what they will like based on the similarity with other users.
several types of collaborative filtering algorithms that Graphbeen uses:
User-User Collaborative Filtering: Here, we try to search, predict and serve products for lookalike customers and offer products based on preferences of other consumers within a large consumer base.
Item-Item Collaborative Filtering: It is very similar to the previous algorithm, but instead of finding a customer look alike, we try finding item look alike. Amazon uses this approach in its recommendation engine to show related products which boost sales.
In a content-based recommendation system, keywords are used to describe the items; besides, a user profile is built to state the type of item this user likes. The idea of content-based filtering is that if you like an item you will also like a ‘similar’ item. For example, when we are recommending the same kind of item like a movie or song recommendation.
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.
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
CAPABILITIES | BENEFITS |
|---|---|
| Machine Learning Based | Elimination of the guesswork of product recommendations |
| Real-time Recommendations | Relevant recommendations since the consumer will still be in the market for the product or service |
| Continuous Machine Learning | A smarter system that updates and improves recommendations increasingly over time |
| Scalability to millions of products and consumers | Increased efficiency of marketing campaigns and cost savings |