Data Science Bringing Web Recommendation Systems to Life

 

Log in to your Netflix account or Hulu account, and the first thing that you will see is the recommendations list. Many a times we watch something that is given in the list because it is something that might be our taste and preference. Do you ever wonder from where do these recommendation lists come from and how they are created? The answer is quite simple -- data. In general terms, the customers generate data and that data in turn is helpful in understanding what they want. This then becomes useful in providing recommendations that the customers like. Big data, data analytics, and machine learning are combined to get the best recommender systems for the businesses.

About recommender systems

Whether it is an eCommerce website, an over-the-top entertainment website or a music streaming application, you can always view a recommendations list. These recommendation lists are the collection of all those products that the business thinks you might like. The recommendation system works based on the customer's past behavior, past purchases and searches. Using the recommender systems, businesses are bringing in more customers, providing them with satisfactory products and indulging in better customer communication.

Data that builds recommendation systems

When it comes to big data, the main resource is data that should be collected and categorized effectively. To create recommender systems and make them more engaging and correct as per the customers’ demands, there is a need to gather the right type of data.

When it comes to recommender systems, there are three kinds of data that are collected. One is the behavioral data that involves search history, purchase history, views on an item, onsite and offsite activities. The second type of data involves all types of contextual data related to the consumer, like the location, referral URL and even the device used. The third type is data is the type of products that are searched and their features, price, style, description, and so on.  All this data is then churned through various models of machine learning and deep learning to get insights into what the customer is searching for, what they might like and which products show the higher frequency to be bought.

Machine learning and neural networks

With proper data sets, the machines can be taught and trained to make decisions and sieve through datasets with much ease. However, one of the biggest problems that many businesses tend to face is the lack of data. This where the neural networks become useful, as they can give predictions and analyze interactions based on minimal data available.

Using data science, the business takes into certain methodologies to formulate the recommendations. Machine learning models focus on the content that the customer likes and then give predictions based on keywords. Also, neural networks focus on customer's similarities, that is if customers have similar indexes, then the chances are that they might like similar products as well. Algorithms then focus on the collaborative filtering and user profiles. However, most of the recommendation engines use the type of filtering as both content filtering and collaborative filtering in given equal importance.

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As the usage of internet and online platforms is increasing, the need to make these platforms more user friendly is also rising. That is the reason why data scientists are in demand and to be a part of this exciting market, one can get trained at a Data science course in Pune.