Recommendations and Ranking
Rank the guide shelf
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
Add each column, then return guide indices in descending order.
Before knowing a reader's interests, Botie can rank the guide shelf using observed openings across readers.
Total each guide column, then return descending guide indices with catalogue order resolving ties.
Examples and results
- Reader-by-guide interaction matrix
1 1 0 1 0 0 - Required result
- 00
- 11
- 22
- Reader-by-guide interaction matrix
0 1 1 0 1 0 - Required result
- 01
- 12
- 20
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
Zero is unobserved interaction, not proof of dislike. Ties use catalogue order. Numerical tests allow a difference of 0.001. Lists and structures must also have the right shape.
A new guide has no readers yet
This check uses the pieces from “Keep guide counts separate while adding readers”.
BOTIE
- Reader-by-guide interaction matrix
0 1 0 1 0 1 0 0 1 0 0 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.
Does a zero entry mean a reader disliked that guide?
Unobserved interaction can reflect missing exposure, so popularity counts do not establish preference.
Zero is unobserved interaction, not proof of dislike. Ties use catalogue order.
Work through the Python challenges →