Introduction

Customers drive sales, and the best way to gain more customers is by understanding their behavioral patterns. To boost sales, e-commerce portals have shifted their focus on data; this is where recommender systems come in handy. A recommendation system helps websites improve their statistics and sales figures by aiming for customer satisfaction.

  1. What is a Recommendation System?
  2. Why Recommender Systems Matter?
  3. Types of Recommender Systems
  4. Recommendation System Examples

1. What is a Recommendation System?

It predicts the user’s interests and recommends products to the users that will engage their attention. These are quite effective machine learning mechanisms that have helped businesses gain more revenue. 

Recommendation systems filter a user’s past behavior through which it creates a structure for the user which is relevant according to the historical data. The machine learning algorithms predict the items a user might like based on the past activity and helps create suggestions which would have a higher probability of the user going for it, and the best-case scenario of actually buying the product.

2. Why Recommender Systems Matter?

Recommender systems have been proved beneficial to both the product sellers and the users, as they reduce the excess costs required for finding the items that are most likely to sell. As the recommendations are based on the likes and interests of users, it aids in their decision-making process.

The system is purely functional based on data; the data is taken from all the websites and applications that include users’ age, history, likes, reviews, and much more that form a basis to form a recommendation that will relate to the user’s persona. 

These are categorized under two sections ‘User-item Interaction’ and ‘Characteristic Information.’

3. Types of Recommender Systems

There are two main types of recommendation systems:

  • Content-based filtering 
  • Collaborative based filtering 
  • Content-Based Filtering 

The content-based filtering process is based on the user’s prior interests and focuses on the attributes of items to create an accurate prediction. While logging into a website, it requires a user to enter age and other credentials which help create better predictions based on user history and activity.

This system works perfectly unless the data collected is false; many times, the users wrongly enter their credentials which leads to wrong predictions. And the other factor affecting content based filtering is if the user is new to the platform, it does not have any actionable data to create recommendations as there is no data to reference it.

The factor that affects the system is that the predictions are limited only to the exact user interests; it is unable to suggest items that would be of similar interest but outside those specific categories. 

How Will It Work?

As explained above, a content-based method is based on user interaction with time. In this case, the model system needs to be built associating with each item. This linking to each item will train the model accordingly and will create recommendations based on user interests and will train the system to evolve with time and be more robust. 

Then train the model-based on the probability of liking the item by the user; it will only account to the interactions considered from the user.

This system would be applicable to model structure that would not be both item or user-focussed.

  • Collaborative Based Filtering

Comparatively collaborative based generates more successful predictions as it is not entirely based on historical data. Collaborative works with the data collected from past item interactions. With the items in the relative interest of the user, it helps create a wireframe that builds on the idea of past suggestions and predicts similar items. This increases customer interaction with the platform as it provides a much more personal touch with personalized recommendations.

The filtering process is further broken down into two sub-categories:

  • Memory-based approach
  • Model-based approach
  • Memory focuses on creating a cluster of users having similar interests, and these clusters would be predicted as a whole based on similar characteristics. As the predictions are accurate for guessing the interests of the users, it has been successful in real-life applications. It quantifies the similarities between the choice of items and not based on the user. It retrieves the data based on activities of the active user; then it selects the most relevant item that aligns with past preferences and then is used for targeting the user. 
  • The model-based approach is backed by data mining and machine learning techniques, where models are trained in sets. These models would be capable of generating suggestions that would be liked by the user. This model-based approach can be implemented across many items, and for many users, this increases the output and increases user engagement.

Important Triggers for Collaborative Mechanism to Work-

Before giving a recommendation, the system needs to have some time to collect actionable data. For instance, for a new e-commerce website, it requires time for users to get acquainted with the user interface and in that period, the systems would analyze the items which have the most interaction with each user.

Same goes for a situation, where the customer is new to the website, it would require a significant amount of time to understand the user behaviour. This phenomenon is denoted as the cold start problem.

These factors, until fulfilled, can deviate the accuracy of collaborative systems.

How Will It Work?

There are two main methods to execute the collaborative method: fully connected neural networks and Item2vec.

  • Fully Connected Neural Network

It is known as the matrix factorization, where the unknown factors of the matrix, be it user or item, are mapped against each other. To learn about the unknown factors, the model will begin by working on random Initialization this process is called embedding.

This will enforce the output of this factorization to match the predictions. This will enable the network to learn by representing both users and items, increasing the accuracy of the predictions.

  • Item2vec

It works on the model based on store purchase order transcripts. This process does not directly involve the users but still directly creates recommendations for every item a user has chosen.

The only factor affecting this system is an abundance of quantifiable data.  

4. Recommendation System Examples

The world’s biggest marketplace Amazon uses recommender systems and is estimated to have increased their purchases by up to 35% as said by McKinsey.

Another e-commerce giant Alibaba also increased its purchases by 20% by implementing effective user-targeting mechanisms from recommendation systems.

Conclusion

Recommender systems are quite important for big-scale companies, whether it serves as a purpose to increase user-engagement or to increase the frequency of customers visiting their marketplace, making them active users.

It is the most bankable source if there is data, one needs to store data to get benefited by the recommendation systems effectively, this will help grow the business and generate more revenue once the user base grows to an exponential level. And this system will help businesses get there.

Though the immense value they carry, they are challenging to evaluate and not beneficial for small scale businesses, as the number of resources required would be affecting the financial sector of the company.

In the coming years, it would prove to be one of the essential systems required to ensure growth and stability to a business. If you are interested in learning more about data processing, our Full Stack Data Science program can help!

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