HOW TO BUILD AN AIPOWERED RECOMMENDATION SYSTEM?
Listen to the article
The internet has transformed the way we shop, with a vast selection of products available for purchase online. However, this convenience comes at a cost, with consumers having to sort through countless options, making it an overwhelming and tiring task.
On the other hand, the challenge for online stores is how to sell more goods
at a higher price and faster than their competitors One solution is to use a recommendation system that utilizes arti몭cial intelligence (AI) to provide personalized recommendations to users Such a system uses machine learning algorithms that analyze user data, such as search history, purchase behavior, and preferences, to predict what products a user is likely interested in.
For consumers, the bene몭ts of personalized product recommendations are obvious. They save time and e몭ort by o몭ering tailored suggestions more likely to match consumers’ interests and preferences. They also help online sellers boost revenue and pro몭t by giving their customers personalized recommendations encouraging them to purchase more products. Besides, they help build customer loyalty and trust by improving the overall shopping experience for consumers.
Hence, personalized product recommendation systems are valuable for online sellers and buyers alike.
Whether it’s e-commerce, media streaming, or any other sector o몭ering content to users, recommending new material is crucial to the platform’s success. Even a small increase in revenue percentage can translate into millions of dollars in pro몭t. In fact, McKinsey estimates that personalized products account for 35% of Amazon’s revenue and a staggering 75% of Net몭ix’s revenue, with the numbers only expected to grow.
Recommendation systems, also known as recommender systems, o몭er a compelling alternative to traditional search algorithms They help users discover new products they might have missed, o몭er personalized recommendations based on their preferences, and ultimately make the shopping experience more e몭cient and enjoyable. These systems can signi몭cantly improve customer satisfaction and drive sales by automating the search process and saving customers’ time. It’s no wonder why every large platform today comes with a recommendation system
Technically speaking, recommendation systems use machine learning algorithms to give users personalized recommendations for products, services, or information based on their behavior, preferences, and history. According to statistics, 80% of customers are likelier to buy from a brand that provides personalized experiences. Furthermore, businesses using a recommendation engine can see a 150% increase in click-through rates, leading to increased sales and revenue. Net몭ix is an excellent example of a company that has leveraged the power of recommendation engines to revolutionize streaming. By providing users with customized content, Net몭ix has achieved lower cancellation rates, saving the company around a billion dollars annually.
But how can you build an AI-powered recommendation system for your business? In this article, we will explore the key components of an AIpowered recommendation engine, the di몭erent types of recommendation algorithms, and how to build and implement a recommendation engine that can enhance user experience and boost your business’s performance Let’s delve deeper and explore this technology.
What is an AI-powered recommendation system?
Types of AI-powered recommendation systems
Bene몭ts of using AI-powered recommendation systems
How does an AI-powered recommendation system work?
How to build a recommendation system? A case study in Python using the MovieLens dataset
What is an AIpowered recommendation system?
An AI-powered recommendation system is a machine learning algorithm that is trained to rank or rate products or users It is designed to predict the ratings a user might give to a speci몭c item and then return those predictions to the user in a ranked list This technology is used by many popular companies such as Google, Amazon, and Net몭ix to increase user
engagement with their platforms For example, Spotify may recommend songs similar to ones you have previously listened to or liked to keep you using their platform to listen to music Amazon may suggest products to users based on the data they have collected on that particular user.
Although recommender systems are incredibly useful, they are often seen as “black boxes” because the models created by these companies are not easily interpretable. Users may not understand why a certain recommendation is being made, but they often 몭nd the recommendations helpful and relevant to their needs and interests. The results generated are often for things the user needs or wants but may not have been aware of until they were recommended.
There are many ways to build a recommender system, and the approaches can vary from algorithmic and formulaic to modeling-centric. These approaches include page rank, collaborative 몭ltering, content-based, and link prediction. However, it is important to note that complexity does not necessarily translate to good performance, and often simple solutions and implementations yield the strongest results. For example, large companies like Reddit, Hacker News, and Google have used simple formulaic implementations of recommendation engines to promote content on their platform.
Identifying what de몭nes a good recommendation is a challenge that many companies still struggle with. The de몭nition of “good” recommendations helps to evaluate the performance of the recommender system that has been built. The quality of a recommendation can be assessed through various tactics that measure coverage and accuracy Accuracy is the fraction of correct recommendations out of the total possible recommendations, while coverage measures the fraction of objects in the search space that the system is able to provide recommendations for. The evaluation method of a recommendation solely depends on the dataset and approach used to generate the recommendation.
Recommender systems share several conceptual similarities with the classi몭cation and regression modeling problem. In an ideal situation, companies would want to see how real users react to recommendations and track metrics around the user to improve their recommendations. However, this is often di몭cult to accomplish.
Types of AIpowered recommendation systems
There are several types of AI-powered recommendation systems, including: Collaborative 몭ltering
systems
Collaborative 몭ltering is a technique used in recommendation systems to predict the interests and preferences of a user based on the data and patterns from many users. The basic principle of collaborative 몭ltering is that if two users have similar tastes in one product, they are also likely to have similar tastes in other products. There are two main types of collaborative 몭ltering approaches: memory-based and model-based.
Memory-based approaches, also known as neighborhood collaborative 몭ltering, use the ratings of user-item combinations to predict their preferences based on their neighborhoods. User-based collaborative 몭ltering recommends products to a user based on the preferences of similar users, while item-based collaborative 몭ltering recommends products based on the similarity between items calculated using user ratings of those items.
On the other hand, model-based approaches use predictive models that involve machine learning to parameterize the features associated with the dataset as inputs of the model This helps to solve an optimization-related problem. Model-based approaches include decision trees, rule-based approaches, and latent factor models
Collaborative 몭ltering models are advantageous because they are easy to implement, provide high-level coverage, and capture subtle characteristics
without requiring knowledge of the item content. Some examples of collaborative 몭ltering algorithms include YouTube’s content recommendations based on users who have subscribed or watched similar videos and CourseEra’s course recommendations based on other individuals who have 몭nished existing courses that a user has completed.
The 몭gure shows the two di몭erent users and their interests, along with the similarity in their tastes. It is found that both Jack and Robert have similar tastes, so Jack’s interest is recommended to Robert and vice versa.
Content-based systems
A content-based recommendation system is a system that generates recommendations based on a user’s preferences and pro몭le by matching them to items they have liked previously. Instead of leveraging ratings between the target user and other users, content-based models focus on the ratings the target user provides. These models establish the level of similarity between items based on the attributes of items liked by the user. To build a
content-based system, you need a strong source of item-level data associated with the attributes of the item, such as price, published year, etc., along with some user feedback on the item, which can be either implicit or explicit. Content-based models are particularly advantageous when insu몭cient rating data is available, as they can leverage ratings and item attributes to generate recommendations. Examples of content-based systems include Amazon’s product feed, which recommends products similar to those the user has previously purchased, and Spotify’s music recommendations. Some companies like Hacker Rank and Reddit have also used algorithmic approaches to recommend new posts to users based on factors such as time of post, number of likes, dislikes, and comments, which can be factored into a formula to generate a score for a post and hence a recommendation.
Launch your project with LeewayHertz
Incorporate a powerful recommendation system into your customer-facing app for enhanced user engagement
The above image shows di몭erent models of the One Plus phone. If a person is looking for a One Plus 7 mobile phone, then One Plus 7T and One Plus 7T Pro are recommended to them.
Hybrid recommendation system
Hybrid recommendation systems are designed to overcome the limitations of individual recommendation systems by combining multiple data sources, which can be achieved through two di몭erent designs: parallel and sequential In the parallel design, multiple recommendation systems are used in parallel to generate recommendations and their outputs are combined to produce a 몭nal result. A single recommendation engine is used in the sequential design, and its output is passed on to the next recommender in the sequence
Hybrid systems o몭er several advantages, including increased robustness and personalization in user recommendations. By combining di몭erent models, hybrid systems can mitigate the weaknesses of individual models, leading to more accurate and diverse recommendations. Net몭ix is a well-known example of a company that uses a hybrid recommendation system,
example of a company that uses a hybrid recommendation system, combining collaborative 몭ltering (based on user behavior) with contentbased 몭ltering (based on item characteristics) to provide more relevant movie recommendations to its users.
Knowledge-based system
A knowledge-based system in a recommendation system generates recommendations based on the user’s needs and domain expertise. It involves de몭ning rules that set the context for each recommendation, such
involves de몭ning rules that set the context for each recommendation, such as criteria for when a speci몭c product or service would bene몭t the user. Unlike the content-based approach, these rules do not necessarily rely on a user’s interaction history but may include other expert information or attributes of customer products and services.
One advantage of a knowledge-based system is that the recommendations can be easily explained, making it easier for users to understand why a particular recommendation was made However, building this type of framework can be expensive, and it is better suited for complex domains where items are infrequently purchased, and data may be lacking One bene몭t of this approach is that it does not su몭er from the same cold-start problems as other methods LeewayHertz
Benefits of using AIpowered recommendation systems
Enhancing customer experience through personalization
Personalization is essential to enhancing customer experience, and a recommendation engine is vital in achieving this goal The Epsilon research report highlights that 80% of consumers prefer purchasing from a brand that provides personalized experiences, indicating personalization can be a signi몭cant competitive advantage for businesses seeking to improve customer loyalty and satisfaction
In today’s world of information overload, customers are inundated with
choices and information, making it challenging to capture their attention. Personalization can help businesses stand out by providing customers with relevant, tailored recommendations that meet their unique needs and preferences. This, in turn, can increase engagement, drive sales, and foster long-term customer loyalty. A recommendation engine can be an e몭ective tool for delivering personalized customer experiences. By leveraging data about a customer’s past behaviors and preferences, a recommendation engine can suggest products or services most likely to appeal to that individual, leading to a more seamless and enjoyable customer journey where customers feel understood, valued, and appreciated.
Ensuring seamless customer experience across multiple channels
Omnichannel marketing has become increasingly popular recently, as customers expect seamless service across multiple channels. However, this presents a signi몭cant challenge for brands – ensuring consistency. According to Zendesk, 35% of customers expect to receive customer service through multiple channels.
An AI-powered recommendation engine can be a game-changer to provide a consistent experience across all channels, including social media, websites, and mobile apps The recommendation engine can ensure that users receive a similar experience everywhere by collecting and utilizing data from customer interactions on di몭erent channels For instance, if a customer searches for a product on your website, the recommendation engine can use that data to suggest personalized products through email or other channels, maintaining a consistent experience.
Deliver relevant content
In today’s digital age, delivering relevant content has become key to customer engagement. Brands that provide personalized content are more likely to attract and retain customers. Recommendation engines play a crucial role in helping businesses achieve this goal by showing customized
and relevant content to their consumers.
One of the best examples of a brand delivering personalized content is Medium, an online publishing platform that allows users to select their preferred topics and, based on their reading behavior, creates customized content lists for them. This approach ensures that users only see content they are interested in, leading to higher engagement rates and increased user satisfaction.
Businesses can tailor their content to each user’s speci몭c interests and preferences using a recommendation engine which can be done by analyzing user behavior and data such as search queries, past purchases and browsing history. With this information, the recommendation engine can provide personalized recommendations for content that the user is more likely to engage with.
Minimizing customer frustration in the customer experience
In today’s fast-paced world, customers have a limited attention span, and irrelevant content or product recommendations can quickly lead to frustration and a negative customer experience and loss of potential sales. An AI-powered recommendation engine can help minimize this frustration by 몭ltering and displaying personalized and relevant content to customers.
By analyzing customer behavior and preferences, a recommendation engine can provide tailored recommendations that are more likely to engage and interest customers, improving the overall customer experience and can lead to increased sales and revenue for businesses. With the help of an AIpowered recommendation engine, businesses can avoid frustrating their customers and provide a more personalized and engaging experience.
Meet customer experience expectations
As customer expectations continue to rise, delivering personalized and seamless experiences has become more important than ever before. In fact,
it has become a key determinant of brand loyalty. A report by Microsoft states that 96% of customers consider customer service and experience as critical factors in몭uencing their loyalty to a brand.
To meet these expectations, businesses can leverage recommendation engines. By analyzing customer behavior and preferences, these engines can provide tailored product recommendations anticipating their needs and desires. This personalized approach helps build stronger customer relationships and drive loyalty over time
In addition, recommendation engines can help to create a consistent experience across all touchpoints, from the website to social media and beyond. By providing personalized and relevant content, businesses can engage customers on their preferred channels and ensure a seamless experience throughout their journey.
Boost business performance using a recommendation engine
As a business owner, you want to achieve higher sales, increase the average order value and ultimately, boost revenue. One e몭ective way to achieve this is by leveraging the power of a recommendation engine. By showing personalized product recommendations to your customers, you can increase the chances of a purchase. An AI-powered recommendation engine can also enhance your up-selling and cross-selling strategies, making the buying process smoother and more convenient for your customers.
By implementing a recommendation engine, you can boost business performance and revenue. With the help of an AI solutions provider, you can e몭ciently integrate a recommendation engine into your business operations and enjoy the bene몭ts of increased sales, higher average order value, and better revenue growth
How does an AIpowered recommendation system work?
A recommendation engine leverages a combination of machine learning technology and data to create personalized recommendations. Data serves as the foundation of a recommendation engine, providing the necessary information to extract patterns. The more extensive the data, the greater the engine’s ability to make pro몭table and relevant recommendations. Recommendation engines complete a standard four-step process:
Step 1: Data collection
To build a recommendation engine, the 몭rst step is to gather data. This can include both explicit data, such as ratings and comments provided by users, and implicit data, such as order history, return history, cart events, pageviews, click-through rates, and search logs. This data is collected for every user who visits the site.
Behavioral data is relatively easy to collect, as it involves logging user activities on the site without requiring extra input However, analyzing this data can be challenging, as 몭ltering out less relevant logs may be necessary. Since each user has their own unique preferences, their data sets will also be distinct. As more data is fed into the engine, it becomes smarter and better at making relevant recommendations This is the same principle used by Amazon’s recommendation engine, which suggests products to users based on their browsing and purchase history, using features like “Frequently Bought Together” and “Recommended for You”.
Step 2: Storing the data
In a recommendation system, the quality of recommendations improves as the volume of data increases. Consequently, recommendation projects often grow into big data projects due to the sheer amount of data involved.
The type of data used for creating recommendations plays a signi몭cant role in determining the appropriate storage type. The choice may include a NoSQL database, a standard SQL database, or an object storage system The selection is primarily based on factors such as the type of data captured,
ease of implementation, storage capacity, integration with other components, and data portability.
When storing user ratings or comments, using a scalable and managed database is crucial to minimize the number of tasks and focus on improving recommendations. Cloud SQL is an ideal option as it caters to these requirements and simpli몭es data loading from Spark Furthermore, with cloud SQL, you can choose to use PostgreSQL, MySQL, or SQL Server for your database, depending on your preference and requirements This 몭exibility is especially bene몭cial for organizations with unique data storage needs.
Step 3: Analyzing the data
The data is 몭ltered using various analysis methods to 몭nd items with similar user engagement data in a recommendation system. Depending on the requirements, di몭erent analysis methods can be used
For instance, if immediate recommendations are to be provided to the user while viewing a product, a more agile analysis is needed. Real-time systems can process data as it is created and is used for giving in-the-moment recommendations. Tools that can process and analyze streams of events are usually involved in real-time systems.
On the other hand, batch analysis requires periodic processing of the data. This approach requires enough data to be created to make the analysis meaningful, such as daily sales volume. A batch system can be suitable for sending an email later.
Another approach is near-real-time analysis, which enables data to be gathered quickly so that the analytics can be refreshed every few minutes or seconds. A near-real-time system is well-suited for providing recommendations during the same browsing session.
Step 4: Filtering the data
In a recommendation system, 몭ltering is an essential step that helps to
retrieve the relevant data required to provide recommendations to the user.
To do so, we must choose an algorithm, such as content-based, clusterbased or collaborative 몭ltering, that best suits the recommendation engine.
Content-based 몭ltering suggests products with similar characteristics to what a user likes or views, while cluster 몭ltering suggests products that go well together, regardless of other users’ behavior Collaborative 몭ltering, on the other hand, makes predictions based on user tastes and assumes that two users who liked the same products in the past will like the same ones in the future.
To represent data about ratings or interactions, we can use matrices with products and users as dimensions. We can then use algorithms such as KNearest, Jaccard’s coe몭cient, Dijkstra’s algorithm, or cosine similarity to relate the datasets of people based on the ratings or products they interact with.
After 몭ltering and using the algorithm, recommendations are given to the user based on the timeliness of the recommendation, whether it’s a real-time recommendation or an email to be sent later
Numerous datasets have been gathered and made accessible for research
and benchmarking purposes with respect to recommendation systems.
Below is a list of top-notch data sources to consider. For beginners, the MovieLens dataset curated by GroupLens Research is highly recommended.
Speci몭cally, the MovieLens 100k dataset is a dependable benchmark dataset with 100,000 ratings from 943 users for 1682 movies. Moreover, each user has rated at least 20 movies. This extensive dataset comprises various 몭les that furnish details on the movies, users, and ratings provided by users for the movies they have viewed.
The ones that are of interest are the following:
u.item: the list of movies
u.data: the list of ratings given by users
Contained within the 몭le “u.data,” are ratings presented in a tab-separated list that includes user ID, item ID, rating, and timestamp. The initial lines of the 몭le are as follows:
As demonstrated previously, the 몭le discloses a user’s rating of a speci몭c 몭lm
This 몭le holds a total of 100,000 such ratings and will be utilized to anticipate the ratings of movies that users are yet to see
