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Put understandable controls next to recommendations and provide a settings area where people can review, change, or reset their preferences. Each control should have a defined effect, and the interface should show whether it changes the current display, future ranking, or a saved preference. Keep intentional feedback distinct from behavior inferred from clicks or views, and explain what data you collect and how it is used.
What should recommendation controls let people do?
Offer actions that describe the outcome rather than asking for a vague like or dislike. Depending on the product, useful choices may include showing more or less like an item, hiding it, declining a topic, or reporting a safety or policy issue. The right choices depend on what your system can actually do.
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Define each action’s scope and persistence before adding it. “Hide this item” might remove only the current result; “show less like this” might affect ranking; “don’t recommend this creator” might save a durable preference. A report should enter a reporting workflow, not silently act as a general preference. Avoid wording that promises a change the system does not make. Google’s People + AI Guidebook recommends aligning a control’s meaning with its actual effect and communicating when that effect occurs.
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Where should feedback appear?
Make it possible to respond where the recommendation appears, without forcing people into a separate settings page for routine feedback. Use a clear text label or an explained icon. Microsoft’s HAX Toolkit recommends enabling feedback on individual outputs during regular interaction; its Guideline 15 says: “Enable the user to provide feedback indicating their preferences during regular interaction with the AI system.”
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When a simple action is not enough to guide the system, offer optional, progressively specific reasons—for example, “not relevant,” “already seen,” or “not this topic.” Keep the first response low-effort, and make follow-up questions easy to dismiss. Google advises keeping requests strategic and minimal rather than interrupting people for feedback on every result.
Different levels of control can coexist. X’s help documentation, for example, describes “Not interested in this post” and “Not interested in this Topic” as distinct feedback options. These are product-specific examples, not a universal required menu; see X’s approach to recommendations.
How should you interpret feedback and behavior?
Store intentional feedback separately from signals inferred from ordinary use. A rating, a click, a view, and a dismissal are not interchangeable: someone might open an item out of curiosity, or dismiss it because they have already seen it. Google warns that interaction with content does not necessarily mean a person wants more of it.
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How do you acknowledge feedback and show its effect?
Confirm that the action was received and make the result visible when possible. If an item disappears, show that it was hidden. If a choice affects recommendations later, explain when the change is expected to take effect. Do not imply that a control trains or updates a model if it only filters what is currently displayed. The Google guide specifically cautions that “show more” or “show less” may change visible content without tuning the model.
Keep the confirmation proportional: a short message or undo option may be enough for a routine action. State any relevant scope or timing in plain language, not in a technical explanation users must seek out.
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How can people review, change, or reset their preferences?
Provide a discoverable place to inspect saved preferences and prior feedback. Let users correct or remove choices, since an earlier selection may have been accidental, made on someone else’s behalf, or no longer reflect their interests. Where appropriate, offer a way to reset personalization to a non-personalized default. Google’s Feedback + Control guidance recommends allowing prior selections to be edited or erased and providing a reset when appropriate.
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How should a conversational recommender handle refinement?
For a conversational system, treat recommendation as a loop rather than a one-shot query. Let the person state a goal, ask a small number of useful preference questions when needed, present options, then accept reactions that refine the next set. OpenDialog’s recommendation documentation describes mixed-initiative, multi-turn recommendation and notes that it depends on user modeling, an organized representation of item attributes, and dialogue management.
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How do you choose the right control design?
Compare controls by the practical trade-offs they create. These dimensions are a useful design framework, not a standardized taxonomy established by one source.
| Decision | Questions to answer |
|---|---|
| Scope | Does the action apply to this item, a creator or source, a topic, the current session, or the whole profile? |
| Persistence | Is the change limited to the current display, temporary, or saved as a lasting preference or model input? |
| Effort | Can the person act in one step, or is a follow-up reason or settings workflow necessary? |
| Interpretability | Can people predict what the control will do from its label? |
| Privacy | Does the action require behavioral logging or profile retention, and can people inspect, edit, or opt out of that data use? |
| Timing | Does the visible feed change immediately, or will an effect appear in later recommendations? |
How do you test whether the controls work?
Evaluate the experience against the promises made by its labels. Test whether people understand each option, whether the observed result matches their expectation, and whether a choice affects the intended part of the system. Also check that the feedback process does not add unnecessary effort and that users can recover from mistaken or outdated preferences.
Quick Recap
- Track explicit feedback separately from inferred engagement signals.
- Check the immediate interface result and any later recommendation effect independently.
- Verify that review, editing, deletion, and reset options are discoverable and behave as described.
- Use product-specific evaluation: there is no universal control set or effect size established by the cited guidance.
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