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Add controls where people see recommendations, make each control’s effect explicit, and let users review or undo their choices. Treat deliberate feedback—such as “show less like this”—separately from inferred signals such as clicks or views. Most importantly, confirm what happened and whether the change affects only the current display, future ranking, or a saved preference.

What should users be able to control?

Offer a small set of clear actions that match the kinds of changes your system can actually make. A control should name its scope and outcome rather than hide them behind a generic dislike button or an unexplained icon.

  • Hide this item: remove the recommendation from the current view.
  • Show me less like this: reduce similar recommendations, and explain whether the effect is immediate filtering, a ranking adjustment, or both.
  • Not interested in this topic: apply feedback to a topic or category rather than only one item.
  • Don’t recommend this source again: save a persistent preference about a creator or publisher.
  • Report: submit a safety or policy concern through a distinct reporting flow; do not imply that reporting is merely a preference signal.

These controls can apply at different scopes—item, source, topic, session, or profile—and persist for different lengths of time. Choose the narrowest scope that fulfills the user’s intent. X’s help page, for example, describes “Not interested in this post” separately from “Not interested in this Topic”; these are product-specific examples, not a universal required menu. X: Our approach to recommendations

Where should feedback appear?

Put a low-friction feedback option in the recommendation flow, close to the item it affects. Use a text label or a clearly explained menu action so people can predict the result. If a follow-up reason would help, make it optional and specific—for example, “not relevant,” “already seen,” or “not this topic.” Microsoft’s HAX Guideline 15 recommends feedback on individual outputs during regular interaction: Encourage granular feedback.

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Do not turn every recommendation into a prompt. Ask for feedback when it is useful and there is a natural opportunity to respond; make requests easy to dismiss. Google’s People + AI Guidebook advises keeping feedback requests strategic, minimal, and easy to dismiss: Feedback + Control.

How should the system interpret feedback?

Keep intentional feedback distinct from behavior inferred during routine use. A click, view, like, or dismissal can have multiple explanations: someone may be curious, may have opened an item accidentally, or may be trying to get rid of it. A positive rating is more explicit than a click, but neither necessarily describes a permanent preference.

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For each signal, define what it means, what it applies to, and what system behavior it is allowed to change. If a signal is ambiguous, give it less influence or use it alongside more direct feedback rather than treating it as a definitive preference. Google cautions that interacting with content does not necessarily mean a person wants more of it. Google People + AI Guidebook: Feedback + Control

How should you confirm the result?

Acknowledge the action and show its actual effect. If the item disappears immediately, say so or make the change visibly. If the preference affects later recommendations only after processing or in a future session, state that timing. Do not promise that a control trains or improves the model if it only filters what is currently displayed.

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Google notes that “show more” or “show less” may change visible content without tuning the underlying model. Describe the behavior your product implements, not an assumed mechanism. Google People + AI Guidebook: Feedback + Control

How can users revise or reset their choices?

Provide a discoverable preferences area where people can inspect, correct, or remove saved feedback. Interests change, and an earlier choice may have been made for someone else; without a way to revise it, an old signal can continue shaping recommendations silently. Where it suits the product, offer a reset to a non-personalized default.

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Explain what behavioral information is collected, why it is used, and where users can inspect or adjust collection settings. Google’s guidance warns against collecting implicit feedback without telling people and recommends giving them ways to edit or erase prior selections. Google People + AI Guidebook: Feedback + Control

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How should a conversational recommender collect preferences?

Make recommendations iterative rather than forcing users to specify everything in one request. A conversational system can ask a small number of useful questions, present options, and let the person react and refine the result over multiple turns. Users should also be able to state a goal directly instead of being forced through a fixed questionnaire.

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This mixed-initiative approach requires more than dialogue management: the system also needs a way to model user preferences and organize the attributes of recommendable items. OpenDialog describes these considerations in its documentation on recommendations.

How do you evaluate whether the controls work?

Test the full path from a user’s choice to the resulting system behavior. The key question is whether people understand what a control does and whether the outcome matches the wording—not simply whether they click it.

  • Check whether users can distinguish item-level feedback from topic- or source-level preferences.
  • Verify that each action changes the intended part of the experience and that its timing is communicated accurately.
  • Measure explicit feedback separately from inferred engagement signals.
  • Check whether people can find and correct mistaken or outdated preferences.
  • Assess whether the controls provide useful influence without adding excessive effort or interrupting normal use.

The right set of controls depends on the product and the consequences of its recommendations; the cited guidance does not establish one universal menu or a universal effect size.

Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.

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