Recommendation System In Artificial Intelligence

Recommendation System in Artificial Intelligence

In the present era, shopping is an irrefutable need of people. If you will conduct a study regarding, you will find the maximum percentage of people loves shopping. However, there is drastic change in way people do shopping. In earlier times, vendors knew about the preferences and needs of customer to make recommendation based on past purchases. In this way, vendor wins the brand loyalty which increase their profitability and sales.

With the vast advancements, online shopping replaces the traditional shopping. Now a king’s size of people shop online rather than going in shops, malls etc. Additionally, recommendations are made by smart recommendation system which is based on AI algorithm.

Apart from this, you can also think this an intelligent salesman who knows customer preference, needs, style and taste to make smart decision about recommendation right things to people. Further, right recommendation increases the conversion and sales which leads to huge profit for some brand. For instance, In YouTube, you will see “Recommendation Videos” on the right side which will recommend you the video you more likely to see based on your previous preferences.

Let’s see what are recommendation system and how does they work?

What are Recommendation Systems?

There is basic definition for recommendation systems is “These are generally information filtering system that filter out the important and relevant information from a enormous amount of dynamically generated information based on user’s past preferences, purchases, interests or
behavior”.

Apart from this, it is one of the major concepts of Artificial Intelligence which has made great advancement in technology.

A Recommendation system can also be consider as target marketing tool for online business especially for E-commerce platform. This filtering system narrows down the selection options for user’s to make intelligent choice to purchase something. Presently, recommendation system has gain wide popularity in various sectors such as retail or e-commerce business, social media.

For example, In Facebook, there is an option “People You May Know” used to recommend people base on mutual friends or other things as well. In Google, you can see “Visually Similar Images” and In Amazon which is one of most popular e-commerce store shows recommendation in “Customer who bought this item also bought.. ”.

How does Recommendation System Works?

Recommendation systems are based on AI algorithm which has the ability to learn and predict most preferred or liked product for user. A typical recommendation system is very intelligent that can work smartly in a dynamic environment. They generally used to past data to see which products are most preferred, purchased or liked by the customers.

Recommendations system are usually consists of four phase which are given below:

  1. Data Collection
  2. Data Storing
  3. Data Analysis
  4. Data Filtration

Data Collection

The first phase for building a recommendation engine is collecting the data. The dataset should be collected for every user who visits the website. However, the data that need to be collected can be in two forms i.e. Implicit and Explicit Data.

Implicit Data is collected from available data stream such as purchase/return history, cart events, search log, click through, page viewed etc. This data is collected for every user who visited the website.

Explicit Data is collected from signup forms, surveys, user’s comments, ratings etc.

Collecting behavior data is much easy because a website contains the log of user activities and this don’t any additional action. Since, every user has different preferences, liking, and disliking about the product therefor the data for every user can be different. Hence, this results in a large amount of information.

Data Storing

For smarter recommendation, you need to feed enormous data to recommendation engine. However, AI algorithm for recommendation system utilizes big data for effective prediction. To store such amount of information you need storage space. Different type of information collected in first phase helps in deciding which kind of storage you need to collect data. There are various ways for data storage such as SQL, NoSQL, Cloud SQL and many more. You need to choose a scalable storage depending upon amount of data you collected, ease of implementation, portability and managing the data.

Data Analysis

This is third phase for building recommendation engine. It is very important to analyses the data to find out the most similar user engagement data such as product, items etc. However, there are various analysis methods available for find out such type of data. One of the analysis
methods is batch analysis which implies vast data is needed to make right analysis. Another way for data analysis is Real-time system that use tool for processing analyzing the events.

Data Filtration

This is the last and most important phase for recommendation system. Here data is got filtered out for making right recommendation. There are various algorithms used for data filtration or we can say recommending data as well. Some of which are explained below:

  • Content-Based : Content based Filtering is based on keywords used by user for describing preferences, items etc. Basically, this filters out the items or products that have similar characteristics to what user liked or viewed previously.
  • Collaborative : Collaborative Filtering recommends the items or products based on similar customers. Basically, it analyzes user’s activities, preferences, behavior and predicts what a user will like base on their similarities with other users.

It usually makes an assumption that the two customers who liked the same product previously, will like to view same ones in the future as well.

  • Hybrid System : As the name suggests, hybrid system are combination of both collaborative and content-based Filtration system. This algorithm follows both characteristics of products which user like or view and also recommend products which are liked by some other user based on their similarity. For instance, Amazon follows Hybrid Recommendation System for making right recommendation for people who visit their website

What are benefits of Implementing Recommendation System?

There are many benefits of implementing recommendation system because a user is more like to purchase at the place where they get maximum help in finding out the right items for them. Most of the businesses implement to maximize their profit. However, some of the other benefits of recommendation systems are given below:

Customer Satisfaction

Recommendation system make the data available from the previous sessions. When users like some product and get recommendation of similar items then they find the better opportunities to find good products. Also, the previous session helps them finding the items they previously liked or viewed. Hence, this increases the customer satisfaction.

Speed Up the Work

Recommendation System saves great time of analyst by serving them the tailored recommendation specific to your needs which helps them in future research. This entirely speed-up their work.

Boost Sales and Revenue :

Research shows that, recommendation system has increased sales and revenue for many businesses such as “Netflix estimated, that its recommendation engine is worth a yearly $1 billion.” Another example could be Amazon, as in when a person click on a laptop for purchasing then you can see combo recommendation below like a screen protector, laptop
cover etc. People will more likely to see and buy that combo as well and hence increases the sales as well.

Additionally, people are more like to see similar items if the recommendations are according to user’s taste. This also increases the conversion rate.

Improve Brand Loyalty :

By continuously catering the right preferences for users, you can
increase the brand loyalty. By relevant personalization, a user feel that they can trust your brand and there are high chances; they will come to your website again.

Helps in Deciding the Right Strategy for Online Marketing Campaign

Recommendation system provides accurate reports of every minute users spend on website and their activities. This allows vendors to make the right decision regarding the marketing campaign. So, they can generate some great offers based on the reports in order to boost their sales.

Conclusion

Recommendation Systems are becoming more popular day by day. Moreover, they are gaining great popularity in field of AI where people are showing their interest in various types of recommendation system such as music, movies, clothing and many more.However, recommendation system provides great personalized experience to users by providing them the tailored suggestions. Additionally, many industries are implementing recommendation system in various sectors such as social media, online retail store, healthcare etc.

In the nutshell, recommendation engines plays an vital role in online businesses and area of Artificial intelligence.

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