Machine Learning Recommendation Systems: Types, Applications, Real-Life Examples, Implementation Techniques & Costs

In the past few years, personalization has become one of the top three reasons why we spend time or invest money in any application and of course have valid reasons for it. In its present stage, the web space – applications and websites alike – know us better than we do. They know what we might buy next, what content we would want to watch, what stocks would we buy, etc. Powered by AI, the ecosystem has cut down our decision making time by a huge extent – a benefit we are reciprocating with increased engagement and spending.

Product recommendation technology – the foundational mechanism behind this user experience shift has not just proved revolutionary for the customers – it is a gamechanger for businesses as well. Something that can be validated from the new-age adage: “with great personalization comes great data”.

Everytime we view, search, or buy something, software with machine learning for recommender systems integrated in their system gets access to our data. Imagine being a business who knows what their customers are searching for and have created a pattern that can push them to buy more, delve deeper into the software – this is nothing short of a goldmine.

In this article, we will dive into machine learning recommendation systems – looking into the multiple facets of the different types, real-world applications, implementation techniques, and the resources that would need to be employed to implement a product recommendation system.

The Common Themes of Recommender Systems Algorithms

As we delve deeper in the article, we will look into multiple types of recommendation systems, implementation techniques, and complexities. Even amidst the differences and varieties, what is common in all the recommender systems algorithms is the theme.

At a macro level there are two important themes that popular recommender systems follow:

1. Understand users through their behavior: An online user today is always connected to the web. With a massive amount of time spent online, users tend to create a rich trail of behavioral activity, ranging from logged in and logged out activity, what pages they spend most of their time on, where they have shown active interest, along with historical activity.

Investment in an Intelligent recommender system using machine learning infers from this high valued data, and produces recommendations which can give businesses insights into users’ deeper interests and make their experience extremely personal.

2. Give recommendations to the user’s context: User contexts come with a variety of intents: based on recent activities, what other users exhibit in the same situations, based on repeat actions, based on attributes and tags, etc. Such latent intentions get amplified through product recommendation algorithms, which both narrows down and widen the scope of discovery and consumption. Instead of one recommendation set that would narrow down and give users limited options, the new generation recommendation system models give users a wide range of relevant recommendations that meet every personalized need.

Types of Recommendation Systems in Machine Learning

Noting how every industry, right from entertainment to social media and eCommerce is using machine learning for recommender systems in order to win the race to become the best user-focused company, there is no one size fits when it comes to the ‘type’.

1. Collaborative Recommender System

This is one of the most popular, widely used, and well-developed technologies in the market. Collaborative machine learning recommendation systems collect and analyze user ratings or recommendations of items, identify similarities between users based on their ratings, and generate new recommendations through user comparisons. The key advantage of collaborative techniques is their independence from any machine-readable representation of the items being recommended, making them effective for complex objects where individual tastes significantly influence preferences.

This method excels in areas like movie and music recommendations, where personal preferences play a crucial role. Moreover, collaborative product recommendation systems improve over time as more user data becomes available, continuously enhancing the accuracy and relevance of their recommendations.

2. Content-based Recommender System

Content-based product recommendation technology is an extension of information filtering research. These systems define objects by their associated features and learn a new user’s interests based on the features present in items the user has rated. Essentially, it is a keyword-specific recommender system where keywords describe the items.

The algorithms in a content-based recommender system recommend items similar to those the user has liked in the past or is currently viewing. For instance, in a content-based recommendation system model, if a user likes action movies featuring a particular actor, the system will suggest other action movies with the same actor or similar themes. This method relies heavily on the quality of the feature representation and can be particularly effective in domains with rich, descriptive metadata about items.

3. Demographic-based Recommender System

This system classifies users based on attributes and makes recommendations according to demographic categories. Many industries favor these recommender systems algorithms because it is straightforward and easy to implement. The algorithms in a demographic-based recommender system start with comprehensive market research in a specific region, supplemented by a brief survey to gather data for categorization. Unlike collaborative and content-based systems, demographic techniques do not require a history of user ratings. Instead, they form “people-to-people” correlations using different data.

For example, an online retail store may recommend products based on age, gender, income level, or location, tailoring the shopping experience to different demographic groups. This approach, which relies completely on machine learning for product recommendation, can effectively introduce new users to the platform by providing relevant recommendations without needing extensive interaction history.

4. Utility-based Recommender System

Another one of the popular types of recommendation systems in machine learning is utility-based systems, which provide suggestions by calculating the utility of each object for the user. The main challenge in this system is creating a utility function tailored to individual users. Each industry may have different methods for determining and applying this function to relevant objects.

A significant advantage of utility-based systems is their ability to incorporate non-product attributes, such as vendor reliability and product availability, into the utility calculation. This allows for real-time inventory checks and displays for the user.

For example, in an e-commerce platform, a utility-based product recommendation algorithm might recommend products based not only on user preferences but also considering factors like current stock levels, delivery times, and promotional offers. This holistic approach ensures that recommendations are not only relevant but also feasible and convenient for the user to act upon.

5. Knowledge-based Recommender System

Knowledge-based recommender systems aim to suggest items based on inferences about a user’s needs and preferences. These systems use functional knowledge, understanding how a specific item meets a particular user need, and can reason about the relationship between a need and a potential recommendation. This type of system is particularly useful in complex domains where user preferences are specific and varied, such as in the case of luxury goods, real estate, or specialized services.

For instance, a knowledge-based travel recommender system might suggest vacation packages based on the user’s specific interests, budget, and travel history, providing personalized recommendations that align closely with the user’s stated needs and preferences.

This system, which uses machine learning for recommender systems, relies on a deep understanding of both the user’s needs and the available options, often requiring detailed user profiles and extensive domain knowledge.

6. Hybrid Recommender System

A hybrid recommender system combines two or more systems to suit a specific industry’s needs, thus defining the standards of recommendation systems in machine learning. The approach is highly sought after as it leverages the strengths of multiple systems while mitigating the weaknesses inherent in using just one type of recommender system.

For example, a hybrid system might combine collaborative filtering with content-based methods to provide more robust recommendations. This dual approach can enhance accuracy by using collaborative filtering to capture user preferences and content-based filtering to ensure that the recommended items match the user’s specific interests.

Another common hybrid approach might involve integrating demographic data to refine recommendations further, offering a comprehensive solution that addresses various aspects of user preferences and behaviors. By combining different types of recommendation systems in machine learning, hybrid systems can offer more nuanced and personalized suggestions, improving user satisfaction and engagement across diverse applications.

Knowing the types of recommender systems using machine learning in the market today is a knowledge set that is half useful – until you know how companies are using it. Let’s solve that next.

Real-World Recommender Systems Examples

While at the back of innumerable benefits ranging from increased user satisfaction, reduced churn, and better inventory management, the technology has become a must-have feature across a range of consumer-focused industries, here are some real-world recommender systems examples that are treated as the holy grail.

1. Netflix

Netflix’s recommendation engine is hands-down built on the most powerful algorithm for machine learning product recommendation. It utilizes an algorithm which analyzes the browser’s viewing history, rating, and search to come up with movies and shows they are likely to enjoy.

[Also Read: How to Create a Video Streaming App like Netflix? Features, Tech Stack, and Costs]

2. Amazon

Amazon’s use of a machine learning recommendation system creates an engine which suggests products on the basis of user’s purchase history, search, and their browsing behavior. Using this, they make personalized recommendations that give users better product choices, investing them deeper in-app.

[Also Read: How Much Does it Cost to Develop a Marketplace App Like Amazon?]

3. Spotify

Spotify’s music recommendation system model suggests music, playlists, and even albums based on the user’s listening history, the songs they like, and search history. It then personalizes recommendations based on the user’s listening habits, preferred genres, and artists.

[Also Read: How Much Does it Cost to Develop a Music Streaming App Like Spotify?]

4. YouTube

YouTube’s recommendation engine suggests videos on the basis of the user’s view history, liked videos, and search. The video/product recommendation system that the platform works on considers factors like user’s favorite channels, the time spent watching a video, and other viewing patterns to then make tailored recommendations.

5. LinkedIn

LinkedIn’s recommendation engine proves to be a useful tool for suggesting jobs, connections, and posts on the basis of a user’s profile, skills, and post engagement history. To ensure that the personalized recommendations work, the model analyzes the user’s job designation, industry, connections they follow, and location.

[Also Read: The Cost, Features and Factors of Developing a Business Networking App]

6. Zillow

Zillow uses machine learning for recommender systems to suggest property seekers real estate properties depending on their search history and pre-entered preferences. They are also given personalized recommendations on the basis of their budget, location, and preferred features – all at the back of the insights gathered from the model.

[Also Read:  What is the Cost of Developing a Real Estate App Like Zillow or Trulia?]

While everything from the types to its applications are different, the implementation technique that an entrepreneur can choose is just one of three.

Recommender Systems Implementation Techniques

The journey of building or implementing machine learning for product recommendation takes an entrepreneur towards three routes, which they have to choose from.

Plug-and-Play Recommendation Engines

Plug-and-play based product recommendation system provides a hassle-free way of incorporating personalized recommendations in your platform. The fact that they are pre-built makes it easy for them to seamlessly integrate into the current infrastructure.

The key benefit of these lay in the ease and simplicity they come with. The engines are intentionally built to be user-friendly, enabling even the non-technical users to set them up through minimal efforts. Some notable examples of the plug-and-play recommendation engines can be seen in Recombee, Seldon, and LiftIgniter.

However, there is a downside to it as well. These platforms come with minimal adaptability and customization. While they do promise speed and comfort, developers might not get the scale of fine-tuning and flexibility a custom solution can offer.

Pre-Trained Cloud-Based Recommendation Services

Cloud-driven recommendation engines enable using the available computational resources to provide easy-to-utilize APIs, which allow ML developers to seamlessly integrate product recommendation technology in their software.

These engines are built to be highly scalable as well, which promises easy handling of large user bases and high traffic volume. Another benefit it offers can be seen through continuous improvement, especially since the base models get constantly updated and refined by the cloud service providers themselves. You can explore this service by partnering with vendors like Amazon Web Services, Google Cloud Platform, and Microsoft Azure.

The key considerations when choosing pre-trained cloud-based recommendation services range from looking into vendor lock-in, data privacy, and customization scope. While the services offer ease and scalability, they do have some restrictions in customizing the recommendation algorithms to align with your key business needs.

Custom Recommendation Engines

Custom product recommendation algorithms offer the most control and flexibility, enabling  incorporation of proprietary algorithms, easy leveraging of domain-centric knowledge, and a careful analysis of your data. Taking this route also leads to capturing the intricate details of users’ preferences, item specifics, and contextual factors that result in accurate and relevant recommendations.

However, although custom recommender systems algorithms come with the highest flexibility among all the three recommender systems machine learning implementation techniques, they call for massive development resources, a team with core expertise in machine learning development, and post-deployment maintenance efforts.

These complexities make it necessary to partner with a machine learning development services firm that would carefully assess and work in tandem with your business needs, present resources, and long-term goals. Although 100 times more beneficial compared to ready-made solutions, custom-built machine learning for product recommendation calls for a dedicated investment.

For the sake of transparency, let us give you an idea of the rule of thumb to follow when making the decision to go custom. Here are the situations when it makes sense.

• You have distinctive business needs: If your company or process has unique requirements, which are impossible to meet through an off-the-shelf solution, choose custom.
• You want complete control and ownership: Creating a custom product recommendation technology would give you 100% control of the complete recommendation generation process: right from data preprocessing, algorithm selection to fine-tuning models.
• You have domain-critical knowledge: If you carry a domain-centric expertise or have access to any specialized data which can massively enhance recommendation accuracy, developing a custom product recommendation algorithm would enable using that skill set effectively.
• Your application calls for high scalability and efficiency: If you foresee getting a huge volume of items or users, have very stringent latency limitations, or simply require complex and large datasets, building a custom machine learning recommendation system would get you the flexibility for designing and optimizing the system for maximum performance and interoperability.
• You want to get a competitive edge: If catching minute details and building accurate recommendations at the back of it is a core differentiator for your application, developing a custom recommendation engine would definitely get you a competitive edge.

THE AUTHOR

chirag

Blockchain Evangelist