Base Item Item Recommender

The base item–item recommender serves as the foundational model for various other recommendation modules. This core model focuses on analyzing relationships between items to predict which products or content are likely to be of interest to a consumer, based on the items they have already engaged with.

It uses behavioural signals such as product views, cart activity, and past purchases to learn which products are frequently interacted with together. This allows the system to suggest items that are closely related to what a consumer has just viewed or bought.

Inspire’s other recommender modules build upon this base model by leveraging its predictions and tailoring the recommendations to specific contexts and use cases.

• Contextual Product Recommendation

• Event Based Retargeting

How it Works

The recommender uses a technique called item–item collaborative filtering. In simple terms:

• It looks at which items are often viewed, purchased, or added to carts together.

• If many consumers who bought product A also buy product B, the model learns that A and B are strongly related.

• When a consumer interacts with product A, the system recommends product B (and other related items).

You can further shape the results with business rules in the Inspire cockpit, by enabling one of the modules above.

Limitations to Consider

• Popularity bias: Frequently purchased items may dominate recommendations, limiting visibility of niche or new products.

• Cold start: New products may not yet have enough interaction data to appear in recommendations.

• Data dependency: The quality of recommendations depends on accurate and complete behavioural data (e.g., product view and purchase tracking).