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.
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.
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’.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
While everything from the types to its applications are different, the implementation technique that an entrepreneur can choose is just one of three.
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 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.
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 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.
| Factors | Custom Recommendation System | Ready-Made Solution |
|---|---|---|
| Initial Cost | High initial development cost | Lower initial subscription or licensing fee |
| Ongoing Cost | Maintenance and scaling costs | Recurring subscription costs |
| Customization | Highly customizable to specific business needs | Limited customization options |
| Scalability | Tailored scalability based on business growth | Fixed scalability parameters |
| Integration | Seamless integration with existing systems | Integration may require additional customization |
| Data Ownership | Full control over data and usage policies | Data policies determined by the provider |
| Performance | Optimized performance for specific use cases | General performance suitable for broad use cases |
| Security | Custom security measures tailored to business | Standard security protocols |
| Support | Dedicated support tailored to the system | Standard support, often shared with other clients |
| Time to Implement | Longer due to development and customization | Shorter, quick deployment |
| Flexibility | Highly flexible to adapt to new requirements | Limited flexibility |
| Expertise Required | Requires in-house or dedicated team of experts | Minimal expertise needed, provider handles most aspects |
| Ownership of IP | Full intellectual property rights | No ownership, reliant on provider’s IP |
| Feature Upgrades | Controlled by business needs and priorities | Dependent on provider’s upgrade schedule |
| Analytics and Insights | Custom analytics tailored to business goals | Standard analytics and insights |
Now irrespective of which one of the recommender systems implementation techniques you end up choosing, there is an active list of challenges you might encounter. This is where partnering with a renowned AI and machine learning software development company comes in handy. A development firm like us.
Having worked with a number of recommender engine development-from-scratch projects and a significant number of implementation assignments, we have gathered an extensive knowledge set for building accurate, effortless products. What this extensive experience has additionally got us is insights and proactive addressal of challenges associated with creating products that elevate the role of machine learning in recommendation.
Although a lot of work has been done in recommender systems, there are still some limitations that need more attention. Most common of those limitations are discussed below in brief:
The problem creates a situation where it gets difficult to make any recommendations. This happens when either the user of the listed item is recently added in the system and very less information is available about them. Because of this, making suggestions or recommendations becomes extremely difficult. Such a situation most commonly occurs in content-based filtering.
In terms of recommendation engines, sparsity is caused by fluctuating, insufficient or frequently varying user ratings. It usually happens when a majority of users do not give ratings and the available ratings are too sparse. Examples of this can be seen in an app’s rating. Users often don’t rate the app unless they face some issue or are highly satisfied, this leads to a situation where the available ratings are very scattered.
Scalability is how well a product recommendation algorithm works when the data volume increases. They, on a default mode, work great when businesses operate with a small dataset, but the moment the dataset expands, managing and monitoring the engine becomes a cumbersome task. Although there are a number of ready-made algorithms which deal with huge and dynamic datasets, the results are often not accurate.
Privacy – especially its safeguarding – remains a hot topic in the recommender system space. The engine takes in a wide range of information from users to give them accurate, reliable, and most organic set of recommendations. This often affects the users’ privacy in a way that the systems end up knowing a lot more about users than needed. Also in case of poorly built systems, users’ data can end in the hands of malicious users.
Over-specialization tends to become an issue when users get similar recommendations based on their previous digital behavior and there remains zero surprise element. Because of this, diversity doesn’t remain in the recommendations pattern and chances for the user to discover something new, which might actually interest them and be beneficial for them becomes almost negligible.
The issue occurs when the user shows inconsistent behavior. This category of users don’t have any set preferences and can interact with one thing at one moment while choosing the exact opposite in the next second. This is mostly seen in media-based applications where we like a huge spectrum of content ranging from romantic to action to documentary, depending on the mood. While natural to us, this lowers the efficiency in recommender systems using machine learning.
A number of engines, even the one we look up to as recommender systems examples, become victims of two attack types – push attacks and nuke attacks. The goal of these is to promote or demote a product rating. To achieve this, attackers build a fake profile and give false ratings to increase or diminish a product’s popularity.
With this, we have looked into several aspects of the recommendation engine and have hopefully given you deep insights into what to expect when you decide to introduce it into your software. As we close this article, let us give you a brief idea on what to expect on the resource and cost front as you build or integrate a recommender system in your application.
Building a recommendation system tailored to your business needs involves several cost factors. Understanding these costs can help you make an informed decision and plan your budget effectively. Here’s a breakdown of the primary cost components:
The initial development phase is one of the most significant cost components when building a custom recommendation system. This phase includes:
Integrating the recommendation system with your existing systems and databases can involve:
Ensuring that the recommendation system functions correctly and provides accurate recommendations involves:
Deploying the recommendation system and ensuring it runs smoothly in a production environment includes:
Ongoing maintenance and support to ensure the recommendation system remains up-to-date and functional:
Other costs that may be incurred during the development and maintenance of a recommendation system:
Based on the above components, the total estimated cost for building a custom recommendation system can range from $90,000 to $300,000+. This estimate varies based on the complexity of the system, the specific business requirements, and the scale at which the system needs to operate.
Consult our experts now to get precise cost estimates as per your specific project needs.
Q. What is a machine learning recommendation system?
A. A recommender system is a software tool that suggests products, services, or information to users based on analysis of data such as user preferences, behavior, and past interactions. These systems are commonly used in e-commerce, streaming services, and social media to enhance user experience by providing personalized recommendations.
Q. How do recommendation algorithms work?
A. Recommendation algorithms analyze user data and item data to suggest items that users are likely to find interesting or useful. There are various types of algorithms, such as collaborative filtering, content-based filtering, and hybrid methods, each using different techniques to process data and generate recommendations.
To understand how recommender systems in machine learning work, these algorithms typically identify patterns and correlations in the data to predict user preferences accurately.
Q. How to create a recommendation system using machine learning?
A. Creating a recommendation system using machine learning involves several steps:
1. Data Collection: Gather user and item data.
2. Data Preprocessing: Clean and prepare the data for analysis.
3. Choosing an Algorithm: Select a suitable recommendation algorithm (e.g., collaborative filtering, content-based).
4. Model Training: Train the model using historical data.
5. Evaluation: Test the model’s accuracy and effectiveness.
6. Deployment: Implement the system in a production environment and continuously update it with new data for improved recommendations.
