User History
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
User History has 10 facts recorded in Dontopedia across 5 references, with 1 live disagreement.
Mostly:rdf:type(5), has initial weight(1), has weight(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (14)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
hasComponentHas Component(2)
- Context Components
ex:context-components - Context Weights
ex:context-weights
hasMemberHas Member(2)
- Context Components
ex:context-components - Context Components
ex:context-components
assignedToAssigned to(1)
- User History Weight
ex:user-history-weight
containsContains(1)
- Context Components
ex:context-components
containsValueContains Value(1)
- Query
ex:query
hasExampleHas Example(1)
- Step 1
ex:step-1
hasKeyHas Key(1)
- Weights
ex:weights
hasPropertyHas Property(1)
- Query
ex:query
includesIncludes(1)
- Context Components
ex:context-components
incorporatesIncorporates(1)
- Contextual Query Reformulation
ex:contextual-query-reformulation
retrievesKeyRetrieves Key(1)
- User History Weight
ex:user-history-weight
targetTarget(1)
- Weight Assignment
ex:weight-assignment
Other facts (9)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Context Component | [1] |
| Rdf:type | Context Component | [2] |
| Rdf:type | Context Component | [3] |
| Rdf:type | Context Component | [4] |
| Rdf:type | Contextual Element | [5] |
| Has Initial Weight | 0.3 | [1] |
| Has Weight | User History Weight | [3] |
| Is Member of | Context Components | [3] |
| Is Component of | Context Components | [4] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (5)
ctx:claims/beam/c8578409-db7a-4511-babf-7af22c569322- full textbeam-chunktext/plain1 KB
doc:beam/c8578409-db7a-4511-babf-7af22c569322Show excerpt
For each combination of weights, evaluate the performance using your test queries and measure the intent precision. ### Example Implementation Here's an example of how you might structure your experiments: ```python import itertools impo…
ctx:claims/beam/c0dac4b7-a8bf-4fc4-b8c0-172938ac7e75- full textbeam-chunktext/plain1 KB
doc:beam/c0dac4b7-a8bf-4fc4-b8c0-172938ac7e75Show excerpt
[Turn 10470] User: I'm trying to optimize the intent precision of my LLM prompts, and I've been experimenting with different context weights. Currently, I'm achieving 88% intent precision on 2,500 test queries, but I want to improve it furt…
ctx:claims/beam/a71afa78-3ac4-4931-987f-ad0a5b6a3f57- full textbeam-chunktext/plain1 KB
doc:beam/a71afa78-3ac4-4931-987f-ad0a5b6a3f57Show excerpt
Identify the different components of your context and assign initial weights. For example: - `user_history` - `current_query` - `system_state` - `external_data_sources` ### Step 2: Generate Weight Combinations Use a systematic approach t…
ctx:claims/beam/17359c4f-ce82-472f-b0cd-20671ade934f- full textbeam-chunktext/plain1 KB
doc:beam/17359c4f-ce82-472f-b0cd-20671ade934fShow excerpt
``` Replace the placeholder functions with your actual logic to evaluate the intent precision. Would you like to proceed with these steps or do you have any specific questions about any part of the process? [Turn 10474] User: Sure, let's…
ctx:claims/beam/ac7a043d-e60a-4397-b105-d32f5733b4fa- full textbeam-chunktext/plain1 KB
doc:beam/ac7a043d-e60a-4397-b105-d32f5733b4faShow excerpt
[Turn 10483] Assistant: Certainly! Contextual query reformulation is a powerful technique that enhances the relevance and precision of search results by taking into account the context surrounding the query. This can include user history, c…
See also
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