Have you ever wondered how some platforms seem to know exactly what you'd like? Meet our Collaborative Filtering algorithm i.e Inspired by your browsing history recommendation algorithm that refines suggestions by analyzing a user's historical browsing patterns, considering both session based and content-based models. this personalized approach enhances recommendations by incorporating insights from the user's past interactions and preferences, contributing to a more tailored and engaging user experience.
How does it work?
This algorithm works by analyzing a user’s past browsing activities.It incorporates both session based and content based models to generate personalized recommendations.
- Session based : This aspect considers the user’s behavior within a session, examining the sequence of pages visited, time spent on each page and interactions during a browsing sessions.
- Content-based model : IT takes into account the specific content or products the user has interacted with, focusing on the attributes or characteristic of those items.
The algorithm identifies patterns and preferences of the customers, offering recommendations that align closely with the user’s browsing history.
Supported Rule Types
- Global
- Home Page
- Product
- Category
- Search
Behavior for Not Logged-in User
IBYBH: Behavior for Home Page, Category, Product, Search, Cart, Checkout or Pages
- If the user has had some interactions in the session, the system will provide recommendations based on products viewed within the session for the IBYBH widget.
- If the user is brand new and has not browsed any products,
- If “FALLBACK” is not enabled, hide the widget when users are not logged in.
- If the “FALLBACK” algorithm is enabled, it will show a widget with fallback algorithm products.
Behavior for Logged-in User
IBYBH: Behavior for Home Page, Category, Product, Search, Cart, Checkout or Pages
- If the user has done some interactions in this or previous sessions, the system will provide recommendations based on products viewed within this and/or past sessions for the IBYBH widget.
- If the user is brand new and has not browsed any products,
- If “FALLBACK” is not enabled, hide the widget when users are not logged in.
- If the “FALLBACK” algorithm is enabled, it will show a widget with fallback algorithm products.
When should you use this algorithm?
Use the “Inspired by Browsing History” algorithm when you want to provide highly personalized recommendations based on a user’s past browsing behaviors, aiming to enhance user engagement by tailoring suggestions to their unique interests and preferences.
Example
Here is an example of how the Inspired by Your Browsing History algorithm could be used in Experro
- A shopper visits an e-commerce website and views a product page for a designer chair.
- Experro uses the Inspired by Your Browsing History algorithm to predict which other products the shopper is likely to be interested in based on their browsing history.
- It displays a list of recommended products for the shopper on the product page.
To create or learn more about widgets, go to our Widget Configurator article and get started.