Feedback is information a user supplies directly or indirectly about a system response. In search, it helps personalization systems learn whether results or recommendations fit changing interests. section 9.5
Search systems can collect feedback in two main forms:
- Explicit feedback records a deliberate judgment, such as a thumbs-up or thumbs-down on a recommendation. section 9.5
- Implicit feedback infers preferences from behavioral signals such as clicks, purchases, and newer interactions. section 9.5
- Real-time feedback lets users refine conversational search responses while an AI agent searches, synthesizes, and adjusts relevance. section 15.4
Feedback creates a loop: collect judgments or behavior, update a personalization profile, and adapt later results or recommendations. Because interests change, newer interactions should influence the system over time. section 9.5
A safer personalization pattern
Personalize only a few top results while retaining non-personalized results, and provide controls to disable personalization. This limits the impact of incorrect feedback-driven inferences and gives users control when privacy or relevance becomes a concern. section 9.5
