What is a recommendation system?
As the name suggests, recommender systems are tools that provide suggestions for products or content a particular customer might be interested in buying or engaging with.
Such a system typically uses machine learning techniques and multiple datasets about items and customers to create an advanced network of complex connections between those products and customers.
What do recommender systems recommend?
A recommendation system can recommend several different things such as products, movies, books, news, articles, jobs, advertisements, and more. For instance, Netflix uses a recommendation system to recommend movies and series to its individual customers.
YouTube recommends different videos to users based on customer profile and watch history. Likewise, e‑commerce website recommend different products to different users based on their preferences.
How recommender systems work
Recommender systems deal with a large volume of information present by filtering the most important information based on the data provided by individual customers (such as user ratings) and several other factors that take care of user preference and interest. Recommender systems determine the match between user and item and impute the similarities between users and items for recommendation.
Role of machine learning
Recommender systems use specialized deep-learning algorithms and machine-learning solutions. Driven by the automated configuration, coordination, and management of machine learning predictive analytics algorithms, recommender systems can intelligently choose which filters to apply to a specific customer’s particular situation. This helps marketers to maximize conversions and average order value.
Recommender system challenges
Several recommendation approaches are in use today. However, comparing their effectiveness is difficult because evaluation results are rarely reproducible. Therefore, the lack of common understanding of reproducibility in recommender systems is challenging.
Three types of recommendation systems
Here are some of the most common methods for recommender systems:
1. Content-based recommender systems
Such recommender systems use filters that are based on similarity of item attributes and use information or features related to the products themselves instead of using users’ preferences.
For example, a recommender might use the year of release, star cast, or genre to suggest movies to the viewers.
2. Collaborative filtering recommender systems
A quite popular recommendation method is collaborative filtering. Such recommender systems use filters that consider the choices and explicit ratings of users. For instance, recommender systems would suggest movies to a viewer based on the previous data of ratings given by different viewers to different movies.
The collaborative filtering algorithm commonly used in recommender systems is matrix factorization. Matrix factorization algorithms work by decomposing the user-item vector interaction matrix into the product of two lower dimensionality rectangular matrices.
3. Hybrid recommender systems
Most modern mobile recommender systems combine both these approaches and are called hybrid recommendation systems.
As a result, they tend to outperform the content-based and collaborative filtering methods. Hybrid recommender systems generate tags based on natural language processing (NLP) for every item and use vector equations to calculate the similarity between items.
Who uses recommender systems
Here are a few industries and businesses that extensively use recommender systems:
Multimedia streaming services use session-based recommendations aimed at predicting the next item based on a sequence of previous items consumed in the session. For instance, Netflix uses session-based recommender systems to suggest movies and web series to individual users.
It is the perfect example of hybrid recommender systems as it takes into account both the interests of the user (collaborative) and the descriptions or features of the movie (content-based). According to McKinsey research, 75% of the content consumed on Netflix is based on machine learning based movie recommendations.
Many dating sites like Tinder use recommender systems to match people. The key factors that contribute are the people that you swiped right on (the people you liked), your reactivation stats, your location, your profiles, and more.
Tinder, in fact, is one of the biggest recommender systems in use with an estimated user base of 50 million people worldwide in 2020.
Facebook is another example that uses product recommendation engines to recommend personalized content to every user profile. It uses multiple recommendation algorithms for different sections.
For instance, the news feed uses one whereas the “people you may know” section uses another. Likewise, the news section, marketplace, Facebook videos, etc are different sections of Facebook, each of which will recommend different things based on your preferences.
Many online stores like Amazon, eBay, and Walmart use recommender systems to suggest products to individual customers based on customer profiles, what they may like, and their purchase history.
In fact, 35% of what consumers purchase on Amazon comes from product recommendations based on machine learning algorithms.
Benefits of recommender systems for e‑commerce businesses
Not sure whether e‑commerce businesses should implement recommender systems?
Here are a few reasons to help you decide:
Better user experience
With effective product recommender systems, users will get personalized and accurate recommendations tailored to their needs. As a result, they will have a good experience and are likely to return to your store. Not only is this beneficial for sales and customer satisfaction, but this can also have a positive impact on your online store's search engine rankings.
Improved sales and conversion rates
Product recommendation engines help online retailers boost sales and increase conversion rates. They allow retailers to upsell or cross-sell their products to increase revenue. With cross-selling products, an e‑commerce store can increase its sales by 20% and profits by 30%.
Decreased cart abandonment
According to Baymard Institute, the average cart abandonment rate across all industries is 69.99%. One driving factor for cart abandonment is improper product recommendations or no recommendations at all. Suggesting personalized products or telling your customers what add-ons they might need with a product can help you tackle this problem.
Increased average order value
A product recommendation system helps increase the average order value of e‑commerce stores by providing a personalized shopping experience to its users. According to statistics, the average order value for a store that shows no product recommendations is $44.41. However, when you show product recommendations and when prospects engage with just a single recommendation, this number multiplies by 369%.
Better customer engagement
Customer relationships are built on trust. Your customers want to feel like your business understands them, and recommending the right products based on customer profiles will help cultivate brand loyalty, inspire more website visits, increase click-through rates, and encourage more interactions with your e‑commerce brand.
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Why choose Recommender
Recommender shows AI-driven product suggestions personalized to every visitor based on their preferences and previous online behavior.
More conversions and repeat visits
Increase average cart conversion by at least 13%. When customers' needs are fulfilled quickly, they are more likely to return to your e-store.
Better average order value
Increase average order value by at least 35%. Recommender offers product tips based on what's already in the customer's shopping cart.
Improved customer experience
Suggest what else individual users might need based on their preferences and similarities with previously seen products.
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Good search engine and recommender, can be tuned up by individual settings in the back office. It is very helpful, especially when you have very large product offer.
Frequently asked questions
How do product recommendation systems based on machine learning work?
Product recommendation systems use advanced machine learning algorithms and deep learning methods to segment customers based on their user data and behavioral patterns (such as purchase and browsing history, likes, or reviews) and target them with personalized product and content suggestions.
Some commonly used recommendation frameworks are content-based, collaborative, and hybrid filtering.
What are the benefits of recommender systems?
A product recommendation system helps you improve user experience and customer engagement on your site by offering personalized recommendations tailored to your customers’ needs. When they have a good experience, they are likely to return to your store.
Such a system also helps increase sales, average order value, and conversion rates by allowing retailers to upsell or cross-sell their products.
What makes a good recommendation system?
Some of the guidelines for recommender systems are:
- It shouldn’t recommend products that are too similar to what users have already seen before.
- It should diversify its recommendations and put more emphasis on personalization.
- It should also strive for temporal diversity, which means it shouldn’t offer the same recommendations in every current user session.
Where do recommendation systems get data from?
Recommender systems often receive data from explicit ratings after buying a product, watching a movie, or listening to a song, from implicit search engine queries and purchase histories, or from other categorical variables about the customers (such as the user profile) or products themselves.
Some recommender systems build a utility matrix, which consists of the rating (or preference) for each user-item pair.
Who is the best product recommendation system provider?
The answer depends on several factors such as your needs, budget, and goals. While you may find several recommender systems on the market, Luigi’s Box recommendation widget uses advanced AI algorithms to take personalization to the next level, helping you achieve +35% average sales increase and +13% average cart conversion increase.
You can place our recommendation widget anywhere on the website. It’ll always feel like its natural part regardless of the e‑commerce platform.