Base User Item Recommender

The base user item recommender serves as the foundational model for various other recommendation modules. This core model focuses on analyzing user interactions with items to generate predictions about which products or content a user may find appealing. It utilizes data such as product views, cart activity, and past purchases to create a comprehensive understanding of user preferences.

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

This includes the following modules:

• Personalized Product Recommendation

• Event Based Retargeting

How it Works

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

• It looks at what consumers with similar behaviour have viewed or purchased.

• If people who bought product A also often buy product B, the model learns this connection.

• Your consumers then get personalized suggestions based on both their own behaviour and the behaviour of others with similar interests.

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

Limitations to Consider

• Popularity bias: Popular products may be recommended more frequently than niche or new items.

• Cold start: New products and new consumers may initially lack enough data for strong recommendations.

• Data dependency: Recommendations depend on the accuracy and completeness of consumer engagement data.