Recommendations and Ranking
Score all candidate guides
BOTIE
Examples, experiments and workspace controls
Connect a tool's inputs to see what it produces. You can try a branch before connecting it to Result.
Try your own inputs
Use a copy of the example to try your own data. Keep the problem's assumptions in mind, and predict the effect of a change before checking the output. You can also add another branch to compare two ways of doing the calculation.
Context for this challenge
Multiply the guide matrix by the reader vector to obtain one score per guide.
Botie has fitted reader and guide factors in a shared coordinate system. One reader vector must score every candidate guide.
Multiply the guide-factor matrix by the reader vector, retaining one output per guide row.
Examples and results
- Guide factor rows
2 1 0 3 - Reader factors
- 01
- 12
- Required result
- 04
- 16
- Guide factor rows
2 -1 - Reader factors
- 03
- Required result
- 06
- 1-3
Try a fresh construction
This clears this draft, including its undo history. Earlier successful attempts stay recorded. Build again without opening hints to record an independent attempt.
Stuck? Inspect a worked construction
The tools below form one possible solution. You can assemble it, follow its signals, and undo that change to return to your own construction.
Assumptions and test precision
Factors must already be fitted from training observations. Scores are not probabilities. Numerical tests allow a difference of 0.001. Lists and structures must also have the right shape.
A latent preference can reduce a score
This check uses the pieces from “Score guide factor rows for one reader”.
BOTIE
- Guide factor rows
2 1 1 -2 -1 3 - Reader factors
- 01
- 1-1
Use a number, text, or an array matching the requested result. For a matrix, put each row in brackets. Saving your prediction runs your current construction on these inputs and checks the earlier examples too.
Build a construction in the workspace above first. This case will test those pieces.
Why should a negative factor score not be treated as a negative probability?
Factor dot products are ranking scores whose interpretation depends on the fitting objective.
Factors must already be fitted from training observations. Scores are not probabilities.
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