About Session-Based Product Recommendations (SBPR)

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System Administrator
In this article

Coveo Machine Learning (Coveo ML) Session-Based Product Recommendation (SBPR) models take advantage of Coveo Personalization-as-you-go capabilities to suggest relevant products based on current user interactions with a Coveo-powered commerce implementation.

Unlike traditional product recommendation models, which suggest products based on co-occurrence patterns using machine learning methods that rely on the frequency of past interactions, SBPR models leverage product embeddings and user session vectors to determine which products to recommend.

SBPR models leverage user session vectors to update the recommendations provided in real time. The algorithm continuously processes the actions taken by the user to recommend products that are relevant to their current shopping session. This is also useful in scenarios where users aren’t authenticated, as the algorithm can infer the user’s intentions as soon as they start interacting with the interface.

Example

During a shopping session on a Coveo-powered sporting goods commerce interface, an unauthenticated user performs the following actions:

  1. Enters Golf iron sets in the search box.

  2. Navigates through the iron sets that are being displayed on the results page, and then clicks on mid-range golf iron sets.

  3. Enters Golf driver in the search box, navigates through the available drivers, and then returns to the home page.

By analyzing the user’s session, the model understands that they’re currently looking for mid-range golf equipment. Therefore, since the recommendations interface on the home page leverages an SBPR model, the products displayed to the current user are complementary products, such as mid-range golf gloves and balls.

Prerequisites

Important

Contact your Coveo representative to enable SBPR in your Coveo organization.

To use SBPR, you must meet the following prerequisites: