Ever wondered how, as soon as you finish a movie on Netflix, there is a new movie recommendation ready for you? When buying products from Amazon, we get additional recommendations for similar products. This is all because of the wonders of machine learning in ecommerce.
The leading statistics portal, Statista.com, reported that online shopping sales 2023 were around $6.3 billion worldwide. With these growing numbers, businesses are trying to implement their best methodologies to increase their sales and recommender systems play a significant role in this.
What is a Recommender System?
A recommender system is an AI algorithm that makes suggestions or recommends more products to customers using big data. These may be determined using many variables, such as previous purchases, search histories, demographic data, and other variables. Users are kept engaged in whatever the platform continues to recommend through recommender systems.
Recommender systems make it convenient for customers to discover products, encourage simplicity in application, and encourage them to stay on the website rather than leave it. The recommender system handles a large amount of data by sorting the essential details on the basis of the details given to it by a user, along with other criteria that take into account the user's choice and interests. It determines whether a user and a product are compatible and then assumes that they have comparable features when trying to make recommendations.
These systems are powered by automated configuration, integration, and supervision of machine learning predictive analytics algorithms, allowing it to make informed decisions about which filters to use depending on the conditions around a given user. It enables marketers to improve conversions and order value averages.
Types of Recommender Systems
Alt text: Types of recommender systems for machine learning for ecommerce
The fundamental objectives of any recommender system are to maintain user engagement, promote products or content, make decision-making easier, and eventually boost demand. There are three basic approaches to developing recommender systems, depending on the techniques and features implemented to determine what products customers are interested in.
Content-based Filtering
The goal of content-based approaches is to attempt to create a model that can account for the recorded user-item interactions depending on the “features” that are already available. By identifying similarities between items, the content-based filtering (CBF) algorithm makes suggestions based on particular features of those items.
These systems build data profiles based on description data, which may include user or item features. In such a recommendation system, items are characterized using keywords, and a customer profile is created that describes the type of products the individual prefers.
Alt text: Example of machine learning in e-commerce as a content-based recommender system
A great example of this recommendation system is how movies are recommended to a user on different streaming platforms. When a user watches a movie, the system will begin searching for different films with similar themes or in a similar genre. When looking for comparable content, a number of key characteristics will be used to calculate similarity.
Collaborative Filtering
Collaborative means of recommender systems are a way of producing new recommendations that are completely based on previous interactions observed among users and products. These recommender systems collect data on previous user interactions and process it to determine which products will be displayed to other regular consumers who share similar likings.
Such a system intends to foresee how individuals will respond to things they haven't yet engaged with. There is a good chance that certain individuals will choose the same products in the future since they have previously made similar choices and purchases.
Alt text: Example of machine learning in e-commerce as a collaborative-based recommender system
Let us understand with a simple example of movies or TV series recommendations on various streaming platforms. A collaborative recommender could recommend something to watch to you if it discovers that you and a different user have similar preferences in the same kind of content.
There are two types of collaborative filtering:
Memory-Based
The memory-based collaborative filtering approach revolves around the idea that forecasts may be derived just from the “memory” of historical data. It uses previous ratings to make predictions about the likes and dislikes of a particular user by trying to identify other users with similar preferences or people who are “neighbors” with that user. Because of this, the approach can sometimes be referred to by the term neighborhood-based.
Although this filtering method is widely accepted for being slightly effective and simple to implement, the range of recommendations is limited. Memory-based collaborative filtering is further divided into:
- User-user collaborative filtering: In general, the user-user method looks for users who share the most similar “interactions profile” to recommend products that are the most well-liked among these nearby users. This approach is described as “user-centered” since it models users according to how they interact with objects and measures the differences between users.
- Item-item collaborative filtering: The goal of the item-item approach is to identify products comparable with those the user has already "positively" engaged with. Two products would be regarded as similar if the majority of users who have interacted with each did so in the same way. This approach is referred to as "item-centered" since it represents objects determined by user interactions and assesses distances between them.
Model-Based
Model-based collaborative methods only consider data related to user-item interactions and presuppose an underlying model designed to cater to these interactions. Model-based algorithms can generate recommendations by collecting individual preferences into a comprehensive machine-learning model of users, objects, and ratings.
The model-based framework makes it possible to identify the fundamental trends in data, adding value across what the model forecasts. Some modeling elements include deep neural networks, matrix factorization, and machine-learning techniques.
Matrix factorization is a collaborative filtering method for representing data regarding interactions and ratings using a series of matrices. The primary concept of the method is to forecast all the unidentified values found in user-item matrices with the current values and account for any inaccuracies.
Deep neural networks are a particularly extensive solution for dealing with the drawbacks of matrix factorization. It enables modeling the nonlinear interactions in the data and finding previously unknown hidden patterns.
The recommender system comprises two neural networks: first, for candidate generation that employs collaborative filtering to select movies based on users' viewing histories, and the other for ranking numerous movies in an ordered manner.
Hybrid Filtering
Hybrid filtering combines collaborative and content-based filtering, maximizing the benefits of each of them. The hybrid models combine numerous recommendation tactics under the same roof to produce recommendations with a higher degree of accuracy and fewer disadvantages than any one of them. Combining the two approaches can solve several challenges, including the initial cold start issue, and accelerate data collection.
How does Machine Learning in E-Commerce and Recommender Systems Work?
A recommendation system is a data filtering system that makes suggestions for new items based on past choices or secondary filtering using deep learning notions and algorithms. Recommender systems are useful tools because they can predict user ratings before the users themselves do.
Alt text: Working of a recommender system in e commerce machine learning
A recommendation system primarily handles data using the following stages:
Data Collection
Find and gather data that is significant to the recommendation system.
Data Storing
Data should be kept in encrypted data warehouses. The type of data storage you should employ can be object storage, traditional SQL database or NoSQL database.
Data Filtering and Analyzing
Filter out undesirable values in the dataset to increase model accuracy. After analysis, the recommender system recommends products with comparable user interaction data. Data analysis involves deep learning or machine learning algorithms that may identify hidden trends and insights.
Model Testing and Deployment
Evaluate the effectiveness of the recommendation system model. Adjust these parameters to achieve the ideal performance if your model doesn't perform well. The model is now prepared to be put into action.
Applications of Machine Learning in E-Commerce
Machine learning applications in ecommerce are not just limited to recommendation systems; there are several other applications of machine learning in ecommerce, such as the following:
- E-commerce businesses utilize predictive analytics as a tool to forecast consumer behavior, such as figuring out what a person is most likely to purchase next or determining interest in particular products.
- Customers can be divided into groups based on key characteristics such as recent purchases, demographic data, and browsing patterns with machine learning. It makes it easy for businesses to customize marketing initiatives and sales promotions to a certain audience.
- Machine learning can help you reduce customer churn by precisely anticipating when consumers are about to leave your platform.
- Massive amounts of data are gathered, stored, or moved, making them a desirable target for hackers. Machine learning can better analyze transactions to determine which ones are authentic and which may be fraudulent.
- A machine learning-powered chatbot is an effective means to provide customers with an exceptional user experience without overloading workers.
Gear Up for the Next Machine Learning Opportunity
Machine learning and AI are the leading technologies in recent years. The leading way to automation is the methods and algorithms to train computer applications to work efficiently and mimic human abilities. Recommender systems are considered the primary and the best examples of machine learning in ecommerce. These systems help both users and businesses in different ways. Several new opportunities are opening up in machine learning, and Interview Kickstart is ready to train you for interviews with well-known tech companies. Sign up for our machine learning course today!
FAQs on Machine Learning in Ecommerce
Q 1. Is AI used in e-commerce?
AI is used in e-commerce for various purposes, such as personalized suggestions and marketing campaigns for each individual.
Q 2. How to apply machine learning in web development?
Machine learning can be widely used in web development to provide smart and tailored user experiences. Using machine learning, it becomes simple and easy to collect and filter large amounts of data to produce user-generated content.
Q 3. What is an example of machine learning in retail?
There are several examples of machine learning being used in retail, such as Zara, Walmart, Shein, Levis, etc.
Q 4. What are the disadvantages of AI in ecommerce?
Some of the disadvantages of AI in e commerce are:
- Lack of creativity
- Security issues
- Increased cyber attacks
- Privacy issues
Q 5. How is machine learning used in the supply chain?
Machine learning can be implemented in the supply chain to estimate the requirements for specific products and eventually notify suppliers to restock.
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