---
schema_version: "1"
id: 19fd33d1323975c079aa08f1aa4cf46aca280eac43506d521a56e2431c8dd8cb
title: Product Recommendations
collection: admin
version: current
locale: en-us
portal_url: https://docs.salesforce.com/admin/current/en-us/cc/264-0-0/b2c_predictive_recommendations.md
canonical_url: https://help.salesforce.com/s/articleView?id=cc.b2c_predictive_recommendations.htm&release=264.0.0&type=5
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content_type: documentation
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description: Use the B2C Commerce Einstein Self Deployment tool to implicitly generate recommendation for products.
products:
  - Commerce
  - B2C Commerce
release: "264"
---
# Product Recommendations

Use the B2C Commerce Einstein Self Deployment tool to implicitly generate recommendation for products.

![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-cc-b2c_merchandiser_administrator-264-0-0-production-enus/ea76f372-6cfa-42a6-8e7c-05c6df7abed8/images/icon_note.png)

Note B2C Commerce considers this approach to be the best practice.

Product recommendations are dynamically generated using B2C Commerce Einstein. You can think of product recommendations as _implicit_ recommendations―in contrast to _explicit_ recommendations that directly link objects together. B2C Commerce Einstein collects data and uses machine learning models to analyze the data and generate product recommendations.

Product recommendations are presented to shoppers as lists of product IDs. Einstein uses business rules—called _recommenders_—to control how these lists appear.