Our product recommendations enhance your website with a modern technique that tailors content to your customers as they shop. By analyzing customer behavior and product details through statistics, rules, or machine learning, these systems provide personalized suggestions that increase engagement, foster brand loyalty, and encourage customers to purchase additional or related products.


Example of recommendations on a website showing a panel with related products.

This section of the documentation will help you understand:


In the rest of this manual, we use the following phrases:

UserVisitor, potential customer, or customer of your web page generating events.
ItemProduct users can buy (these terms are used as synonyms).
PersonalizationEffect of treating each user individually, e.g. design texts/colors using Jinja or recommend relevant items to users according to preference.
Recommender systemAny approach to choose items for users, usually in a personalized way. Also known as the “recommendation model” or the “recommender system”. The abbreviation RS is used in further texts.
PlacementLocation within the webpage where you can integrate recommendations (e.g. homepage, product detail, basket…).
EngineDeployed RS as a service with a unique ID. This ID can be found as the last part of the URL when you view the RS model in Bloomreach Engagement (the process of designing is explained later). For more information see Where to find the ID campaign_id.
FallbackAny (usually non-personalized) recommendation strategy when the main engine fails. A typical situation is when the engine is broken or a new user appears (so no data has been tracked).
Similarity scoreA number between 0 and 1 that expresses the similarity between two items.
Top N itemsAn ordered list of top N items according to their similarity score to a given item.
InteractionThe connection between user and item represented as foursome {user, item, time, action}.

An example of interaction would look like this:


Formal Introduction

Formally, we can define the recommendation problem in an e-commerce world as follows:
“Given all data about users and items (metadata, interactions), find the best items for a given user to recommend that will maximize metric relevant for the business.”

Most of the algorithms in Bloomreach Engagement are based either on catalog metadata (like description, categories, etc.) or collected user-item interactions (events like purchase_item or view_item).

Recommendation systems can be roughly divided into four segments according to their ability of personalization and whether they are based on some simple heuristics (rule-based) or machine learning algorithm (AI model). You can see the table below:

Rule-basedArtificial Intelligence (AI) / Machine learning (ML) models
Non-personalizedSimple statistical methods of searching for the best items suitable for everyone.
E.g., top-selling, brand-new items.
Trained AI model mostly on catalog metadata.
E.g., attributes matching, text model, image model, bought together items.
PersonalizedSimple statistical methods executed individually for each customer.
E.g., last seen items, customer recent interactions, etc.
Trained AI model on user-item interactions, usually in combination with other RS.
E.g., collaborative filtering,