6 Dynamic Challenges in Formulating the Imperative Recommendation System

  • Neelam Tyagi
  • Oct 26, 2019
  • Big Data
6 Dynamic Challenges in Formulating the Imperative Recommendation System title banner

The current era is fully evolved with computational technologies with the latest and significant trends in Business Analytics in making an efficient business decision. Also, the advent of the internet changes our day-to-day life as we utilize computation technologies for not only our business but also to make life more convenient. 


It is possible to make life easy going through computer solutions and the internet, this blog throws lights on how computer solutions improve daily life activities that are directly related to business productivity and hence left us unanswerable. 


Business analytics plays a crucial role in finding computer solutions, it is a complete procedure of data collection, analysis, and organizing a smart system in order to make critical business decisions. Data has reached everywhere in worlds through the internet and easily accessible. An online-based business environment follows the same procedure, i.e business analytics with the help of the recommendation system, you will learn how the recommendation system has a crucial role for individuals and online businesses. 


The recommendation system is widely used in online businesses such as online retails, advertisements, mobile applications, and social networking sites. These sites generate a huge amount of real-time structured and unstructured data every second. 


For this kind of heterogeneous data, a strong computational and analytical data mining solution is required which in turn helps to build strong judgments, here a recommendation system is one of the strong computation solutions for big data that can arrive at conclusions which in turn benefits with better business decisions. 


Have you ever shopped with Amazon, Netflix or any other e-commerce sites? you must have experienced a recommendation system there, this analyzes the past purchasing behavior and makes recommendations in real-time while you’re shopping.  Let’s understand the working of the recommendation system and the role of analytics in it.


Recommendation System


With the deployment of the recommendation system in different business applications, it is highly likely that individuals purchase an item which is suggested by recommendations in wide areas of the market where there is a variety of choices for users like in fields of food, gaming, music, movies, etc. 


A recommendation system deals with the user profile and related data for suggesting items of user interest. With the digital era, the e-commerce sites recommend items based on ratings or reviews given by users to an item. The recommendation system can be noticed in the place when several users associate with many users or many users associate with many items/products. 


The recommendation system is the process in which data is collected data from different sources, stored datasets, filtered the most relevant information about items and provide to users by discovering patterns in a dataset. It uses the past behavior of customers and provides recommendations based on ratings/reviews.


A set of algorithms is built for the smooth functioning of recommendation systems. It studies the data to predict the interests of different users in available items and according to that recommends items to users. 


The recommender uses sets of attributes to recommend different items to different numbers of users. The basic function of the recommendation system is to look and provide products in which a user is interested the most. 


For example, when a user visits e-commerce sites such as Amazon, Flipkart, Netflix, Apple music store, etc some products recommended to the user. The recommendation is based on user purchases or the product of his interest in the past.


The system or application is operating this action is called a recommendation system. This, in turn, saves the time of the user and provides the best product of their choice or preference which could lead to an increment in the business growth.


This image is showing the functioning of the recommendation system, recommender function acts on the combination of user data and new product attributes and predicts the ratings for the user.

 Recommender Function



Suppose a retailer does not have a previous history of customer choice and interest then the retailer recommends him the item most in demand or the product by which the retailer gets maximum profit. 


On the other hand, in order to maintain the interest of visitors for their frequent visits to the store, the e-store would possibly recommend items of his interest and needs. So, the recommendation system algorithms are designed for the achievement of smart business.


Based on user surveys and evaluations, recommendation systems can be characterized  into two parts;


  1. Content-based recommendation system 

           Content-based filtering is an approach that uses the descriptions of what users viewed or bought in the past, an item recommended based on the similarities of previously used items.  


  1. Collaborative filtering recommendation system

          Collaborative filtering is another approach that uses user history and activities for recommendations. It identifies the different relationships between users and products and predicts different items for users.[1] 


Best recommendations are required for groups or individuals, and hence analytics is needed to solve severe computational processing speedily. 


The recommendation system has a wide range of applications that can be observed in retail stores, videos on demand and music screening. It is widely used in movies, news, stocks, social group tags, music, knowledge-based apps, social media platforms, research articles, trading support systems, etc. 


For example, social connections recommendation by facebook, LinkedIn or Instagram, recommendations of dates by dating applications, banking, and insurance products recommendations, healthcare and retail product recommendations, and game recommendations in Xbox. 


Challenges in developing an autocratic recommendation system


  1. Cold start

This problem arises when new users or new items are added to the system, a new item can’t recommend to users initially when it is introduced to the recommendation system without any rating or reviews and hence it is hard to predict the choice or interest of users which leads to less accurate recommendations. For example, a newly released movie cannot be recommended to the user until it gets some ratings.


A new user or item added based problem is difficult to handle as it is impossible to obtain a similar user without knowing previous interest or preferences. 


  1. Sparsity 

It happens many times when most of the users do not give ratings or reviews to the items they purchased and hence the rating model becomes very sparse which could lead to data sparsity problem, it decreases the possibilities of finding a set of users with similar ratings or interest.


  1. Synonymy

Synonymies arise when a single item is represented with two or more different names or listings of items having similar meanings, in such condition, the recommendation system can’t recognize whether the terms shows various items or the same item. For example, recommendation systems predict ‘action movie’ or action film’ the same.


  1. Privacy 

Generally, an individual needs to feed his personal information to the recommendation system for more beneficial services but it causes the issues of data privacy and security, many users feel hesitation to feed their personal data into recommendation systems that suffer from data privacy issues. 


The recommendation system is bound to have the personal information of users and use it to the fullest in order to provide personalized recommendation services. To deal with this issue, the recommendation systems must ensure trust among their users.


  1. Scalability

One biggest issue the scalability of algorithms having real-world datasets under the recommendation system, a huge changing data is generated by user-item interactions in the form of ratings and reviews and consequently, scalability is a big concern for these datasets. 


Recommendation system interpret results on large datasets inefficiently, some advanced large-scaled methods are required for this issue.


  1. Latency 

We observe many products are added more frequently to the database of recommendation systems, only already existed products are recommended to users as newly added products are not rated yet. 


So an issue of Latency arises. The collaborative filtering method and category-based approach in combination with user-item interaction can be used to deal with this issue.




For sure, the recommendation system is unturned to exist in the e-commerce businesses with the help of collaborative or content-based filtering to predict different items and yes, users are most satisfied with the products recommended to them. The recommending system provides new ways of finding or extracting personalized information on the internet. 


It enables users to have access to the products and services easily within limited periods of time. There are the most advanced methods in order to design high-quality and fine-tuned recommendation engines such as machine learning, deep learning, neural networks, etc. it is well worth saying recommendation systems improve the usage of machine learning along with big data using business analytics. For more blogs in Analytics and new technologies do read Analytics Steps.




Neelam Tyagi

A versatile and creative technical writer in Analytics Steps. She has cross-functional experience in qualitative and quantitative analysis. She is focused and enthusiastic to achieve targets on time.

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