Last Updated on by Kumar Raja

How Data Is Processed By Recommender Systems In Data Science?

Before Data Science rose to its popularity, traditional Hadoop & Spark technologies were used for collecting the data, however the sheer volume of information that has to be mined from this data has presented major a problem for these technologies. This is the reason that led to the rise in the popularity of Data Science. Data Scientists make use of their human intuition to come up with accurate decisions based on the insights from Big Data. If you are curious to know about the Big Data analytics process in Data Science, then join us for our institutes advanced Data Science Training In Hyderabad program to learn the analytics techniques in real-time.

Over the years, the advancements in Data Science have happened very rapidly giving many crucial applications to businesses like Customer Sentiment Analysis, Predictive Analytics, Recommender Systems & more. Recommender Systems have become very crucial for the e-commerce industry. In this post, let’s take a look at the data processing technique followed by these recommender systems to come up with accurate product recommendations for the users.

The Data Processing Technique In Recommender Systems-

Data Collection-

The very first phase of the data processing technique in the recommender systems involves gathering the relevant data for analysis. The data in this process comes in two different types namely explicit and implicit data. Data which is collected from multiple user ratings and comments are explicit data whereas the data collected from the user log, history, clicks, page views, etc relates to implicit data.

Data Storing-

Recommender systems require algorithms that have to be trained using the available data. So data storing is very crucial for these systems to function accurately & based on the requirement we can use NoSQL database or a standard SQL database for storing.

Data Analysis-

The Recommender systems make use of the following types of analysis techniques

  • Real-Time System Analysis

This method of analysis is ideal for creating split second recommendations to the users. The analysis technique does its work as soon as the data is made available includes

  • Near-Real-Time Analysis

This analysis method is used to present the user with recommendations within the same browsing session. This system gathers data from the users in every few minutes or seconds & analysis for user behavioral for accurate recommendations.

  • Batch Analysis

The analysis method is mostly used recommendations in e-mail marketing. The data collected from the users is also helpful for analysis like daily sales volume.

Data Filtration-

This step involves filtering the data to come up with accurate user recommendations. This method makes use of advanced Machine Learning algorithms & the different types of filtering techniques include content based filtering, collaborative filtering & cluster based filtering.

If you are interested in building a recommender system on your own, then join us for our advanced Data Science training program & work on capstone projects in real-time.

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