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Recommendation systems – why you shouldn’t do it on your own

by Akash Bhatia, July 4, 2014

As the Web evolves and data piles up, personalization is crucial. Recommendation systems allow for personalized user experience based on various characteristics. But, it is important to note… One does not simply build a recommendation engine!

Recommendation engines are heavy duty data science systems that provide users with a set of products/items based on different data points. This is not something one can whip out with a couple lines of code. It requires the technical knowledge to transform raw data into meaningful and useful information.

There are two basic approaches to building a recommendation system: collaborative filtering and the content-based approach. The first being a method which makes recommendations based on what other people with the similar interests to the user would have liked. The second is method which makes recommendations based on what products have similar properties to what the user is viewing at the moment. But, sometime they can be used together. This is no easy task! Considering it is merely a feature of product, the amount of resources that this requires to build and maintain can be quite a pain… and it probably still won’t be able to match the state-of-the-art engines. This is where the products from Infinite Analytics come in handy!

At Infinite Analytics we create an ecommerce customers’ Social Footprint based on the user’s social graph, and use predictive analytics on the Social Footprint to personalize the online users’ shopping experience and provide actionable insights for our clients.

Our NLP, machine learning and semantic technologies also helps us establish relationships between users, brands and stars who endorse those brands and that way we build structured data from which we can extract meaningful information for users.

The beauty of our solution is that there is basically no effort for integrating this on the retailer’s end!

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