Before learning about the recommendation systems in Python, we must remember a particular statement made by the founder of Apple, Steve Jobs. He once said that people do not exactly know what they are looking for unless you show them. Now, if we go to a movie streaming platform or even an eCommerce website, the website provides us with relatable recommendations and offers.
These recommendations appear by following a systematic procedure known as Recommendation Systems, and engineers need machine learning to create this platform-specific recommender system. This system aims to offer a personalized service to the users. According to Accenture, 91% of customers select brands that offer a personalized experience, and machine learning is the main domain through which you can offer the same.
What Are Recommendation Systems In Machine Learning?
Programs that provide recommendations to multiple users depending on various parameters are known as recommender systems. The primary goal of these algorithms is to forecast the product that customers will be most interested in and likely to buy (depending on their previous purchases, behaviors, etc.). Recommendation systems are used by lots of companies, including the likes of Netflix, Amazon, and others, to assist their customers in finding the ideal product or movie for them.
In a recommender system, a huge amount of data is available, which is filtered by the system itself and the most crucial information related to the users is collected. Then the recommender system considers the other available criteria, which are based on the previous choices made by the users. It determines whether a user and an item are compatible and then assumes that they are similar to make recommendations.
The recommender systems have assisted the companies in improving their quality of services as well as in the decision-making process.
In this article, we will walk you through the basics of the recommendation system machine learning project, how it works, different types of recommendation systems, its benefits and the main guidelines you need to follow to use this ML-based system.
5 Modern-Day Examples Of Recommendation Systems
The objective of recommendation systems is to personalize the user experience by providing relevant and useful recommendations. There are various techniques used in recommendation systems, including collaborative filtering, content-based filtering, and hybrid methods.
- Netflix: Netflix uses a recommendation system to suggest movies and TV shows to users based on their viewing history, ratings, and search history.
- Amazon: Amazon's recommendation system suggests products to users based on their browsing and purchase history, as well as items that are frequently bought together.
- Spotify: Spotify's recommendation system suggests songs and playlists to users based on their listening history, favorite songs, and playlists.
- YouTube: YouTube's recommendation system suggests videos to users based on their viewing history, search history, and interactions with the platform.
- TikTok: TikTok uses a recommendation system to suggest videos to users based on their viewing history, interactions with the platform, and the popularity of the video.
Recommendation systems are widely used in e-commerce, entertainment, and other industries to improve the user experience and drive sales.
The number of things you can recommend through recommendation system machine learning examples is limitless. Practically it can be applied almost anywhere. Be it movies, advertisements, books, news, jobs, articles, etc., recommendation systems can benefit the company and the end users.
Benefits of Recommendation System (Machine Learning Project)
Since the companies like Amazon, Spotify, Netflix, Amazon, YouTube, etc. have implemented recommendation system machine learning projects in their platforms, they have experienced a drastic improvement in their services and sales.
1. Increase in Sales By Improving User Experience
By offering personalized customer service to the user with the help of recommendation systems, companies can increase their overall sales. More importantly, they can retain their customers for a long time, receive a large number of orders, will be able to sell more products on every order, and experience a better average order value.
2. Better Customer Service
In a virtual setting, recommendation algorithms assist in simulating in-store customer service. As a user, you will also receive a customized buying experience from a genuine representative who offers knowledgeable advice to any hesitant or doubtful buyer.
3. Increased Revenue or Income
By the previous sales reports published by big companies like Amazon that have already implemented recommendation systems, it has already been established that recommendation systems can potentially increase companies' revenue by increasing sales.
In 2019, McKinsey published an article named ‘The Future of Personalization’, which emphasized that recommender systems can boost sales by 5% -15% while increasing brand awareness by 10% - 30%.
4. Helps in the Decision-making Process
With the help of recommender systems, companies can collect a lot of information about the users, including sales data, customer behaviors, etc., with which they can make detailed and fact-driven reports. This valuable information can help the company’s decision makers to adjust their performances on multiple aspects such as logistics, advertising, marketing, product pricing, etc.
5. Get the Actual Product
While visiting a website, customers or users often experience information overload, which makes them extremely confused. However, with the help of a recommender system, companies can avoid them, and it will only focus on the products that the customers are interested in.
Different Types of Recommendation Systems
Based on the approaches taken into consideration, different recommendation systems are available. Some of the commonly used recommendation systems are discussed below:
Collaborative Filtering
Collaborative filtering is a recommendation system where multiple users with the same traits and buying tendencies are grouped and receive product recommendations based on their similarities.
The entire focus of the recommendation systems is on customers' behavior, history, the type of products they have purchased, etc., instead of focusing on the products themselves.
The machine learning algorithms used in the collaborative filtering process are matrix factorization, clustering models, Bayesian networks, K-nearest neighbors, etc. These algorithms discern the likes and dislikes of the customers and promote things that can interest them, customers.
Advantages: A major advantage of the collaborative filtering process is that it is usually extremely precise. Most recommendation systems use this method by increasing their parameters, and matching the similarities to offer better performance.
To make advancements in this particular approach, context-aware collaborative filtering considers contextual information (for example, date, time, locality) and user data to offer more precise recommendations in the future.
Another advantage of collaborative filtering is predicting a user’s interest in an item. Many times, users do not know that a product exists. Still, using the similarities of other users, the recommender system offers the products to other users to catch their attention and simultaneously increase their sales.
Content-based Filtering
Although content-based filtering differs from collaborative filtering, it has many perks of its own. In this approach, the focus is on the products and not the customers, which is why the system designs are also different.
The primary focal point for this system is the product’s price, type, features, categories, etc., all of which have individual keywords related to the product. The number of reviews and purchases determines the likability of the products.
Depending on the associated parameters, the machine learning algorithms used in content-based filterings, such as clustering, Bayesian classifiers, decision trees, etc., will find out about the product purchase patterns, and it will also suggest products with similar features to the customers.
Advantages: The main advantage of this strategy over collaborative filtering is that it alleviates the existing challenges with newly released items because the system already has a wealth of knowledge about each product's characteristics owing to the specified keywords.
Guidelines to Follow when Implementing a Recommender Systems
By now, you must have understood that recommender systems are machine learning-based sophisticated instruments with elaborate designs. Things may become extremely difficult if you do not know how to use them properly. Here is a list of things you need to keep in mind while implementing a recommender system
1. Page Context-driven Strategies
For different pages of your website or platform, the recommendations strategy also needs to be fairly different. For example, many companies offer the most popular products on their standard homepage. Still, if you keep surfing, you will come across another suggestion made by the recommender system, similar products. On the other hand, you can also use a bought-together product strategy just before the customer enters the payment gateway page.
2. User-oriented Plans
Depending on the customer journey stage, the best recommender systems can make appropriate suggestions to attract the customer and retain their attention span. For example, suppose a customer is visiting your website for the first time. In that case, your recommender system can show them the best-selling or most popular products, whereas, for a seasoned customer with a lengthy history of purchases, your system can offer them goods that suit their needs and taste (by following their purchase history).
3. Already Available Solutions
If you are willing to invest a significant amount of money, you can create your tailor-made recommendation system for your platform. However, you also need to note that the upfront cost of developing a brand-new recommender system will be equally high. However, there are lots of custom solutions available in the market which can also be used. Some of them are Optimizely, Evergage, IBM Watson Real-Time Personalization, Adobe Target, etc.
4. Different Approaches
There are multiple approaches you can undertake to create a recommender system, and their parameters and functionality will also be different. Therefore, before undertaking a specific approach, you must carefully consider your target audience, the type of product and services you are trying to sell and the company’s circumstances.
In a market that is becoming more dynamic and customer-focused, everyone is looking for ways to serve their customers better, which is why many companies are trying to implement recommender systems in their platforms. With the help of recommendation systems companies can not only drastically increase their sales, but at the same time, they can also offer a tailored customer experience.
A McKinsey report says AI and machine learning have furthered this synergy by enabling businesses to tailor consumer journeys from start to finish.
On the other hand, given that machine learning-powered systems need enormous volumes of customer information to function properly, these technologies may easily conflict between the need for high performance and the increasing concerns of the general public and legislative bodies for privacy and data protection issues.
You also must note that these recommender systems are often considered black boxes because although they are excellent at analyzing data to find links and patterns, nobody truly understands how they arrive at a certain decision. The other challenges will undoubtedly be addressed with appropriate data management regulations if recommendation systems are to provide consistent advantages without jeopardizing user privacy.
Enjoyed this article? Leave a rating!
Loading...
