6 Dynamic Challenges in Formulating the Recommendation System

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 computational technologies and intelligence 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 in the terms of Recommendation System improve daily life activities that are directly related to business productivity and hence left us unanswerable. 

 

Introduction

 

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. You can understand the 5 steps data analysis process form here.

 

Data has reached everywhere in worlds through the internet and can be 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 like Instagram, or for LinkedIn Analytics. These sites generate a huge amount of real-time structured and unstructured data every second. 

 

“The invisible pieces of code that form the gears and cogs of the modern machine age, algorithms have given the world everything from social media feeds to search engines and satellite navigation to music recommendation systems”.-Hannah Fry

 

For this kind of heterogeneous data, a strong computational and analytical data mining or text 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 such as JIOMart? 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.

 

What is the Recommendation System?

 

A Recommendation System depicts a system, is capable of anticipating the future preference/recommendation of a set of items/products for a user, and recommends the top items. It 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. It can be noticed in the place when several users associate with many users or many users associate with many items/products. 

 

Under this process, data is collected from different sources, stored datasets, filtered the most relevant information about items and provided 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 and studies the data to predict the interests of different users in available items and according to that recommends items to users. 

 

“To understand the limits and opportunities of algorithms in the context of artistic creation, we need to understand that the latter usually consists of three elements: discovery, production, and recommendation.”-Evgeny Morozov

 

Additonally, 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. 

 

Examples

 

For example, when a user visits e-commerce sites such as Amazon, Flipkart, Netflix, JioMart, 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 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.

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Recommender Function

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Types of Recommendation System

 

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, and then an item is recommended based on the similarities of previously used items.  

 

Under this  process, additional information is used about users/or items. For example, a movie recommender system uses additional information as age, sex ,job and any other personal information of users and the categroy, main actors, duration and other relevant characteristics of the movie (item).

 

Therefore, content based methods build a model on the basis of “available features” that describes user-item interactions.

 

2. Collaborative filtering recommendation system

 

Collaborative filtering is the method for a recommender system that uses user history and activities for recommendations, or say past interaction between users and items to make new recommendations. Such interaction is known as “user-item interaction matrix”.

 

Collaborative filtering simply identifies the different relationships between users and products and predicts different items for users. Therefore, in the collaborative method, past user-item interactions are adequate for identifying similar users/ items and create predictions depending on such estimated proximities. 

 

The main advantage of this approach is that it does not require any information about users or items and hence can be used in many circumstances.

 

The more users interact with items, the new recommendations become more accurate for a fixed set of users and items as new interactions, listed over the time, carry new information and hence make the system more efficient.

 

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

 

Also Read | Review-based Recommendation System

 

What are the Applications of 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.

 

The recommendation system has a wide range of applications that can be observed in;

 

1. Retail stores, videos on demand and music screening. 

2. It is widely used in movies and film industry , news, stocks, social group tags, 

3. Knowledge-based apps like Byju, social media platforms, research articles, trading support systems, etc.

4. Social connections recommendation by facebook, LinkedIn or Instagram, 

5. Recommendations of dates by dating applications, in financial services, and insurance products recommendations, 

6. Healthcare and retail product recommendations, and game recommendations in Xbox. 

 

Also Read | Top Machine Learning Algorithms

 

What are Challenges in developing 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. 

 

2. 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 problems, it decreases the possibilities of finding a set of users with similar ratings or interest.

 

3. Synonymy

 

Synonymy arises 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.

 

4. Privacy 

 

Generally, an individual needs to feed his personal information (have an experience with hyper-personalization) 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.

 

5. Scalability

 

One biggest issue is 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 systems interpret results on large datasets inefficiently, some advanced large-scaled methods are required for this issue.

 

6. Latency

 

We observe many products are added more frequently to the database of recommendation systems, only already existing 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.

 

Also Read | Top Deep Learning Algorithms

 

Conclusion

 

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 the high-quality and fine-tuned recommendations engine 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 analytics.