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You can use thumbs-up and thumbs-down feedback to suggest keyword-weight changes, but treat those suggestions as evidence to review—not automatic truth. The practical loop is to log which terms contributed to each score, attach explicit feedback to that record, wait for enough observations, generate small bounded proposals, and have a person approve them before deployment.
Why attribution has to be captured when scoring happens
A final score does not reveal which terms produced it. If you keep only the score and later collect a reaction, you cannot reliably tell which keyword should receive credit or blame. Record term-level attribution as part of the scoring event.
For each result, retain the query or task context, item identifier, timestamp, matched terms, each term’s score contribution, the active weight version, and the resulting score. Link later feedback to this attribution record. This gives reviewers a traceable path from a reaction back to the terms and weights involved.
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Explicit feedback is a user’s judgment about relevance in a particular situation, not an objective label. The result’s context and the person providing the feedback can matter. Preserve user or visitor context when appropriate, while distinguishing it from anonymous aggregate feedback.
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Amazon Kendra, for example, accepts explicit relevance labels such as RELEVANT and NOT_RELEVANT and supports associating feedback with user or visitor context. Its feedback and incremental-learning behavior are specific to that service; API support can vary by index type, so check current AWS documentation before building against it: Amazon Kendra feedback submission. Kendra’s documentation says that in its own system, “The boost decreases over time, so if users stop selecting a result, Amazon Kendra eventually removes it and shows another more popular result instead.” That behavior is not a general guarantee for other ranking systems.
Do not assume that every feedback button changes a ranking. Google Search Help, for example, states: “Note: The feedback you give won’t influence a page’s ranking in results.” That is a product-specific statement, not a rule for systems you build.
A cautious workflow for proposing weight changes
- Log a scoring event. Persist the context, item, timestamp, matched terms, per-term contributions, active weight version, and total score when the score is calculated.
- Attach feedback to that event. Record the positive or negative signal against the result and its attribution record. Keep enough context to tell user-specific signals from anonymous aggregates where that distinction matters.
- Aggregate only after a minimum evidence threshold. Count positive and negative reactions by term, and optionally by query segment when different kinds of queries behave differently. A minimum threshold makes a proposal less sensitive to a handful of reactions; it does not make the evidence unbiased or conclusive.
- Generate a small, bounded proposal. One implementation heuristic is to compare a term’s positive share among its eligible feedback with configured decision bands. If evidence clears the minimum and the share falls into a configured band, propose a small increment or decrement, then clamp the result between configured minimum and maximum weights. The bands, step size, and bounds are parameters to tune and validate for your application—not universally established values.
- Put the proposal in a review queue. Show the term, current and proposed weights, positive and negative counts, evidence window, and expected effects on score distribution. Let a knowledgeable reviewer approve, reject, or defer it. Record the reviewer, decision, and weight version for an audit trail.
- Evaluate approved changes. Check them against a held-out or curated judgment set, or use an online evaluation design suited to the system. Compare performance across query types rather than relying on the same reactions used to create the proposal.
Why the formula should remain a heuristic
A count-based proposal rule is attractive because it is easy to inspect, but the signal can be misleading. People may react to the presentation, the order of results, or their own context as well as to relevance. Clicks are especially ambiguous: a click does not necessarily mean a result was useful.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallResearch on learning to rank explains how position and presentation bias can distort click-based labels, and studies propensity weighting as one correction: Joachims, Swaminathan, and Schnabel, “Unbiased Learning-to-Rank with Biased Feedback”. That work concerns particular methods and experimental settings; it does not establish that a simple thumbs-based weight update will improve every ranking system. Explicit thumbs feedback removes some ambiguity present in clicks, but can still reflect who responded and how the result was presented.
Weights also interact with one another and with the score threshold that determines what is shown. A term-level proposal can change the distribution of scores for many items, not just the item that received a reaction. As Craig Solomon puts it in the tutorial that motivates this workflow, “Feedback is an opinion about relevance, and relevance is a business judgement.” The formula can surface a decision; it cannot replace that judgment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When manual weight proposals stop being enough
Manual proposals fit a scorer with a small, interpretable set of weighted terms and feedback that can be attributed to those terms. If ranking depends on many interacting signals, or the fixed list of weights no longer captures useful distinctions, learning to rank (LTR) is a possible next step. LTR systems use query-document examples, features, and relevance judgments or suitable behavioral evidence to train or apply a reranking model.
| Approach | Interpretability and data | Bias and operations |
|---|---|---|
| Manual weight proposals | Individual term changes are straightforward to inspect. They require attributable feedback for the terms being adjusted. | Simple counts can inherit exposure and presentation bias. A small scoring script can produce proposals without an LTR stack, but still needs review and evaluation. |
| Learning to rank | A model combines features, so its behavior is less directly reducible to one weight. It needs query-document examples, features, and relevance labels or suitable behavioral judgments. | Click-trained approaches may need bias correction. Feature extraction, training, and deployment introduce additional tooling and operational work. |
OpenSearch documents an LTR plugin for feature and model workflows: OpenSearch Learning to Rank. Elastic documents judgment lists, feature extraction, training, and inference, and recommends balancing examples across query types and including positive and negative examples: Elastic learning to rank. These systems do not remove the need for good judgments or careful evaluation; they provide a more capable ranking approach when a short list of manually adjusted keyword weights is no longer adequate.
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