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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsE-commerce personalization usually starts with a record of which products a shopper viewed or purchased, linked to a user or pseudonymous ID and a timestamp. The right data depends on the task: a popular-items list, a related-products panel, and recommendations for a particular shopper do not all use the same events. Product and customer attributes can add context, but should be collected only when they serve a defined purpose and the relevant permissions allow their use.
Start with the recommendation you want to make
Before choosing fields, decide what the shopping experience should do. Amazon Web Services (AWS) documents different event inputs for different recommendation use cases, so a generic “collect everything” event stream is not a sound starting point. AWS’s e-commerce use-case guidance maps common placements to interaction data.
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| Shopping feature | Relevant interaction data | What to know |
|---|---|---|
| Most-viewed products | Product view events | AWS lists view events for its most-viewed-items use case. |
| Best sellers or frequently bought together | Purchase events | AWS lists purchase events for these purchase-based use cases. |
| “Customers who viewed this also viewed” | View events; AWS recommends purchase events as input for this use case | Use the event mix that fits the model and the placement, rather than assuming views alone are sufficient. |
| Recommendations for you | A user identifier plus interactions, such as views and purchases | AWS lists item and user datasets as optional for this use case; the user ID is still needed to request recommendations for a particular user. |
These are examples from AWS documentation, not universal requirements for every recommendation system. Check the chosen model’s data contract and validate that its inputs support the intended placement.
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Interaction events
For each relevant event, record an identifier for the user or pseudonymous shopper, an item ID, an event type such as View or Purchase, and the event time. These fields let a system connect a shopper’s activity to products and distinguish browsing from buying. Use consistent item IDs across event records and the catalog; otherwise, the system may not be able to reliably match an interaction to the product it concerns.
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User identifiers
A system needs a stable identifier within its own data model to connect a shopper’s events and, where supported, return recommendations for that shopper. AWS requires a USER_ID in its user metadata dataset and uses a userId for its “recommended for you” request. That does not mean a merchant must use a real name or email address: choose an identifier that fits the privacy design, and limit access and use to the stated purpose.
Product catalog information
Maintain item IDs and accurate product records. Depending on the system and feature, item attributes and an item creation timestamp may help describe products or support item-aware recommendations and filtering. AWS lists the item dataset as optional for its “recommended for you” use case, so do not treat every catalog field as a universal prerequisite. Keep catalog information current enough that recommendations do not point shoppers to unavailable or materially misdescribed products.
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User attributes
Some systems accept numerical or categorical user metadata, such as age or loyalty membership. AWS says selected user metadata can improve relevance and, in supported cases, help recommendations for people with no interaction history. Its user dataset requires at least one numerical or categorical metadata column. That is a product-specific schema requirement, not a reason to collect sensitive or irrelevant attributes: include a field only when it has a clear role in the experience and is permitted for that purpose.
How much interaction data is enough?
There is no universal interaction threshold for e-commerce AI. For the documented Amazon Personalize e-commerce use cases, AWS specifies a minimum of 1,000 item interactions from at least 25 unique users, with at least two interactions per user. In a separate Amazon Personalize data-readiness checklist, AWS recommends at least 50,000 interactions from at least 1,000 users, with two or more interactions each, for quality recommendations.
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The first figure is an AWS product-specific minimum; the second is AWS guidance for quality, not a guarantee of results. Neither is an industry-wide rule or a promise of higher conversion. A different model, use case, or provider may set different requirements, and a merchant should check the documentation for the system it actually plans to use.
Use metadata carefully for new or low-activity shoppers
New visitors and shoppers with little recorded activity present a cold-start problem: there may be too few interactions to infer individual preferences. Selected user metadata may help some supported recommendation models make suggestions without that shopper’s own interaction history. It does not remove the need to meet the model’s requirements or establish that an attribute is useful, accurate, and appropriate to use.
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For shoppers without sufficient history, merchants can also distinguish personalized recommendations from broadly useful placements such as popular products. Keep the experience honest about what it is doing; a best-seller list is not an individualized prediction simply because an AI system displays it.
Make consent and purpose part of the data design
Permission is not one blanket switch for every use. Shopify’s Customer Privacy API checks preferences, analytics, marketing, and data sharing separately. The result can depend on a shopper’s region, merchant settings, and consent. In regions configured to require consent, non-essential purposes are not allowed by default until consent is given.
Shopify cautions that collecting data without consent may fail to comply with applicable law and recommends consulting counsel about which rules apply. Its app privacy requirements call for explaining data collected from Shopify APIs, merchants, and customers; how it is used; retention; and whether processing occurs outside Europe. These are Shopify platform requirements and guidance for apps, not a complete legal analysis for every merchant.
- Define the recommendation or shopping-experience purpose before collecting a field.
- Check permission separately for each applicable purpose, such as analytics, marketing, or sharing.
- Explain what is collected, how it is used, how long it is retained, and where it is processed.
- Set a retention rule appropriate to the purpose and support applicable data-subject requests through the relevant platform and legal process.
The cited guidance does not establish a universal retention period. Applicable legal duties depend on jurisdiction and context.
Choose an implementation path that fits the job
Amazon Personalize documents domain-based and customizable recommendation approaches, real-time recommendation requests, and batch recommendations and user segments. AWS recommends starting with an e-commerce domain dataset group for an e-commerce application; a custom dataset group lets a user choose a recipe and configure a model for real-time or batch use. These are options within AWS, not a comparative ranking of recommendation providers.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →When evaluating an implementation, compare the things that affect whether it can serve the intended shopping feature:
- Use-case fit: Does it support popular items, similar items, purchase bundles, or recommendations for a known user?
- Data readiness: Do you have enough relevant interactions, distinct users, and the view or purchase events the approach expects?
- Update path: Do you need recommendations to respond to new events in real time, or can batch results meet the experience’s needs?
- Privacy integration: Can your platform and chosen service enforce purpose-specific permissions and your data-handling rules?
Documentation for one service cannot establish which provider is best, what recommendation lift to expect, or which contractual protections are available across the market. Assess those questions against the actual product, deployment, and terms being considered.
Quick Recap
A practical data-planning checklist
- Name the placement. Specify whether the goal is popular products, related items, frequent bundles, or recommendations for an identified shopper.
- Map the needed events. Start with consistent item IDs, a suitable user or pseudonymous ID, event type, and timestamp; include purchase events where the use case calls for them.
- Check data volume against the selected system. Treat published thresholds as specific to that provider and model, not as universal benchmarks.
- Add context selectively. Decide whether product attributes or user metadata solve a real need for the chosen experience.
- Verify permissions and disclosures. Confirm each data purpose, explain collection and use, define retention, and account for applicable platform and legal requirements.
- Test the result against the shopping task. Verify that outputs match the placement, use current catalog data, and behave appropriately for shoppers with little or no history.
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