There are hundreds of micro-decisions each one of us makes on a day-to-day basis. If we dive into our actions, we can classify these as cautious or incautious. Artificial Intelligence techniques aim at imitating such human actions by some art of data modeling.

To understand this better, let’s take a hypothetical scenario.  Assume that you are in a pre-internet era, and you want to buy elegant attire for an upcoming festival. How do you choose which store or its website to visit? Which brands to look for? What are the latest trends concerning the product?  We may ask people close to us to suggest or share feedback and make a final call based on it.

Now let’s investigate our current ecosystem. The present day’s recommendation system does the exact thing for you without having any physical existence. These systems are more robust in making personalized recommendations. While using online services like Amazon, Netflix, Goodreads, etc. whenever you see something like ‘Recommendations for you’ or ‘User who viewed this also viewed this, there is a recommendation engine running in the background. According to a report from McKinsey, personalized marketing and recommendation-based algorithms are already disrupting marketing space in the retail sector. To quote this report,
“Already, 35 percent of what consumers purchase on Amazon and 75 percent of what they watch on Netflix come from product recommendations based on such algorithms.”

According to this article, the recommendation system market will grow from USD 588.9 million in 2016 to USD 4414.8 million in 2022. It quotes,
“The recommendation engine is expected to grow at a Compound Annual Growth Rate (CAGR) of 40.7% at least till 2022.”

Zensar Recommendation Platform

The recommendation system can be tricky to design and scale-up. These systems need a lot of effort to solve real-time problems like cold start (actions when a new user or new item comes in inventory). To reduce the effort required in development, we have created a platform equipped with all the tools that support a robust recommendation system. Here are some salient features of our in-house recommendation platform.

  1. Plug-in Ready: We designed the platform to adapt to a variety of data to develop a system out of both structured and unstructured data. It has novel methods that can handle the cold start problems more efficiently.  This platform includes all the machine learning training modules for a more powerful performance.
  2. Customizable Platform Although this platform includes standard state-of-the-art training modules, and it can be tailored to train on customized modules.
  3. Multiple Training Modules: This platform is designed to include all in-built training algorithms. The training modules include advanced machine learning and deep learning-based algorithms. The training modules include all classes of the recommendation algorithms like content-based recommendation algorithm, collaborative filtering or a hybrid algorithm.
  4. Deployment Ready: Our ‘recommendation system is easily scalable. The system can be deployed on the cloud as well as on-premise.
  5. Integration: Our system can run as a standalone or can be integrated into existing platforms such as chatbot, websites, etc.
  6. Ease of Use: The platform has an interactive environment that can be used with very little training. The intention is to provide a solution that requires minimal knowledge of data science.

We have designed the platform to cover every possible recommendation event or need. The platform can give recommendations based on item-item similarity, user-item similarity, as well as user-user similarity. This platform also considers external data from social media, where it can capture every possible user behavior, both explicit and implicit.

Application to Domain

Our recommendation system platform is designed to be readily used across all verticals. Let’s discuss some domain-specific use cases.

  1. Retail: Retail and e-commerce are perhaps the biggest beneficiaries of recommendation systems. Recommendation engine as support will help deliver a better user experience and higher customer satisfaction. Additionally, highly personalized recommendations will help in generating better revenue.

    2. Banking, Finance & Financial Services: Catering to a variety of customers can be a challenge for any industry. A financial or a banking product can be highly prevalent in one geography and fail to be impactful in another. In the age of personalized marketing, our recommendation system can help generate user-specific offers.

    3.Hi-Tech Manufacturing: Manufacturing has a lot of potential problems, which can be dealt with AI (as general) specific solutions. The issue of resource planning in a manufacturing plant can be one use case where recommendation engines can help. Capacity planning is a field where a machine learning algorithm can be highly beneficial.

Business Impact

As stated earlier in this blog, the recommendation engine market is set to grow considerably.  Businesses have slowly begun to realize the true potential of AI. The investments in this field have seen a steady rise in the past few years. This instates a positive sentiment of the market towards AI/ML. Keeping the receptiveness of the market in mind, we can say that our recommendation platform will directly impact the adoption of such systems across all domains. If you would like to know more about our recommendation system, please leave a comment below.

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Saurabh Katariyar

Posted by Saurabh Katariyar

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