Recommenders are intelligent systems designed to analyze user preferences, behaviors, and interactions to provide personalized recommendations. These systems utilize various algorithms and techniques to suggest relevant content, products, or services to users, aiming to enhance their browsing or shopping experience. By leveraging data such as purchase history, browsing patterns, and demographic information, recommenders offer tailored suggestions that align with individual user preferences, increasing engagement, satisfaction, and ultimately driving conversions.
But there are two different types of recommenders : AI vs non-AI recommender.
Non-AI recommenders
This type of recommendation suggests content based on explicit user or programmatically entered data and metadata. It is fairly limited since it can only make use of two filtering algorithms : content-based filtering and knowledge based filtering.
This recommender has two major recommendation types :
- items-to-item : the recommender will suggest similar products to the one the visitor is already viewing by matching the keywords in the titled, the user-selected tags, the seller, etc…
- items-to-user : the recommender will suggest products based on the user’s history. This means that if a majority of the products that the customer has bought are explicitly in the category “Phones”, the recommender would suggest products in category “Phone accessories” (since it is a child of the “Phones” category).
So as you can guess, this type of recommender is limited since it doesn’t make use of context or meaning, it doesn’t learn and adapt, it only matches keywords.
AI-powered recommenders
On the other hand, this type of recommender is a more complex form of technology since instead of simply matching keywords to suggest the most relevant content to the user, it will use machine learning algorithms and techniques to adapt to the users’ behaviours and needs to suggest the most relevant content possible. This means that this AI recommender will truly know the customers as people instead of a compilation of properties.
The AI recommendation model can learn about the users by training on the user events that are collected whenever a user interacts with the website. AI recommenders can make use of the most filtering algorithms : content-based filtering, collaborative filtering and hybrid filtering.
One of the reasons AI recommenders outperform traditional (non-AI) systems is their effectiveness in helping visitors discover content. Unlike non-AI systems, AI recommenders predict user interests rather than suggesting content after users have already found similar items. This is made possible through collaborative filtering, which analyzes all visitor events to predict trends.
Additionally, AI recommenders can leverage knowledge beyond raw data. For instance, they can consider factors such as holidays, geolocation, and semantics. This broader understanding enables them to offer more relevant content, as their decision-making is informed by a richer dataset.
If you want to use an AI recommender for your website but don’t know which one is right for you, you could read our recommender comparison based on pricing.
