A recommendation engine is a powerful tool for increasing your customer loyalty and improving the shopping behavior on your e-commerce platform. According to McKinsey, 35% of what consumers purchase on Amazon and 75% of what they watch on Netflix come from product recommendations. AI recommendation engines are the future of personalization, so don’t miss your chance to take your business to the next level.
What is a Recommendation Engine
Let’s start with the recommendation engine definition. A recommender system with AI is a system that suggests products, services, information based on the user data. The recommendation algorithm retrieves such data as the user's history and the behavior of similar users, their preferences, interests, and buying experience. Based on this data, a recommendation engine provides an efficient way for e-commerce companies to provide consumers with personalized products and services and cloud recommendations ai examples.
So if you have enough data on your customers and the items they buy you can implement a recommendation system right away. The type of AI recommendations will depend on the following factors:
- Type of data you have about your users/items
- Your ability to scale
- The transparency of your recommendations
What is a Recommendation Engine and How does it Work
So how does AI recommendation engine work?
First, it collects data
Usually, there are two types of data recommendations AI system collects: implicit and explicit. Implicit data includes information collected from user activities such as web search history, clicks, cart events, search log, and order history. Explicit data is collected from customer behavior, such as reviews and ratings, likes and dislikes, and product comments.
Recommendation engines also use such customer data as demographics (age, gender) and psychographics (interests, values) to identify similar patterns between different customers, as well as feature data (genre, item type) to identify similarities between products.
“Polls have shown some really powerful numbers in regards to AR too: 35% of people say that they would be shopping online more if they could virtually try on a product before buying it, and 22% would be less likely to visit a brick-and-mortar store if AR was available via their favorite e-commerce store. AR grants a person the ability not just to see a 3D model of a product but lets a user see how it looks if they were actually wearing it. Some products and industries lend themself better to traditional shopping methods, but AR is going to shake things up sooner than later.
— Michael Prusich, Director of Business Development at 1Digital Agency.
Then, this data is stored
You need scalable storage that will grow together with your data. Over time, the amount of data will only increase, so make sure you have different types of storage for each data set that is scalable and reliable.
Data is analyzed
After that, the data is processed and analyzed in different ways: real-time analysis (data is processed as it is created), batch analysis (data is processed periodically), and near-real-time analysis: Data is processed in minutes after it’s gathered.
“Technology and data analysis helps your people focus time and effort on their areas of strength and adds more value.”
— Sayer Martin, Director of Product Management at Conga.
Data is filtered
At the final stage, the data is filtered using different matrixes or mathematical rules and formulas depending on the model of recommendation filtering. The outcome of this filtering is the recommendations.
Recommendation Engine Algorithm Categories
The process of predicting the utility of items for a particular user depends on the recommendation algorithm selected. There are three main categories of recommendation engines: content-based, collaborative filtering (CF)-based, and knowledge-based approaches. Let’s review each of them.
Content-Based
As you may have guessed from its name, content-based recommender systems use the content of an item’s description based on a user’s profile. Content-based recommender systems recommend items that are similar to items the user has been previously interested in.
First, the AI recommender systems extract data from documents/descriptions. For example, a movie consists of the attributes such as genre, the director, writer, etc. Content-based recommender systems mostly use a keyword-based model known as the vector space model with term frequency-inverse document frequency weighting.
For example, Pandora, the online streaming music company, has engineered features from all songs in their catalog as part of the Music Genome Project. They use traditional machine learning techniques to analyze a certain user's probability of liking a specific song based on a training set of their song listening history.
Demographic Based
If you have a lot of demographic information about your users, you may recommend products or services based on similar users and their past behavior. Like the content-based method, the suggestion engine derives data set for each of your users and generates models that predict probabilities of liking certain things. Keep in mind that this requires a lot of information about your users. If you don’t have a large amount of such data, try collaborative filtering.
Collaborative Filtering
This method is based on the relationship between users and items, and the interesting thing here is that you don’t need information about the users or the items. All you need is a rating of some kind for each user/item interaction: either explicit (rating or like) or implicit (click or view).
There are two types of collaborative filtering: user-user and item-item. The user vector includes all the items purchased by the user and the rating given for each particular product when the item is all about finding similar items to the ones user was already interested in.
Benefits of Recommender Systems
The product recommendation engine retrieves data to understand your website visitor's shopping behavior better and automates your business. The main benefits include:
- Understand the behavioral patterns of your website visitors during their whole session on your platform and provide them with better content recommendation
- Collect shoppers’ behavior and compile the data for the analytics process to use it for the proper recommendations.
- Understand and interpret your website's social interaction and value to your customers.
- Better evaluate their preferences and the products purchased via a particular social network. You can use this for targeting a specific demographic to better understand the real needs of your users.
- The product recommendation engine can also monitor the cart abandonment activities to help you interact with the users and don’t let them go without a purchase.
Recommendation Systems with Machine Learning
Machine Learning (ML) is the AI field concerned with using computers to simulate human learning, allowing computers to identify and acquire knowledge from the real world and improve their performance on some tasks based on the new knowledge. Today, ML algorithms are used in different industries, such as business, advertisement, and medicine, divided into supervised, unsupervised, semi-supervised, and reinforcement learning. Supervised learning happens when algorithms are provided with training data and correct answers.
Recommenders can be evaluated similarly as classical machine learning models on historical data (offline evaluation). In reality, using one algorithm for creating a deep learning recommendation system will not deliver the desired result, so it’s better to mix them to solve different problems.
Any system based on Machine Learning principles requires initial data to learn. If there are no such data, the system will not be able to deliver results. In recommendation systems, this problem is known under the “cold start” name. Each algorithm requires a certain set of data, and errors may arise when launching the service in production with the minimal number of users and absence of algorithms. There are a few solutions to the problem. So, the items can be recommended by the date of adding to the cart or randomly choosing them from the whole item set, hoping the user will like some of them. Second, the system can be started after the service works for a while, and there will be enough data to analyze.
AI Recommendations Examples
If you are looking for the type of recommender system to implement, here are the most common use cases:
Related product recommendations
You have certainly seen it when purchasing one product and then seeing a “You may also like” section with similar recommended products. This type of AI-based recommender engine can analyze the individual purchase behavior and detect patterns that will help provide a certain user with suggestions of products that will most likely match his or her interests.
Alternative products recommendation
Online stores may suggest similar products when the product is out of stock. It’s mostly combined with a “notify me” feature when a product appears in stock again, such an ML-based recommender engine brings great results!
Weekly products recommendations
Recommending the top items is a popular strategy used by music streaming services. It generates the most popular items/services/products to grow customer loyalty and improve conversion rates.
Not sure what type of recommender system to choose? We at Proxet have over a decade of experience in developing state-of-the-art software solutions for startups, SMBs, and enterprises. 60% of our projects have AI/ML components, including everything from chatbot implementation to image recognition and sentiment analysis. We expertly guide our clients through their digital journey to success, so don’t hesitate to contact us for a free consultation!
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