Powered by

13.Decision Paths

Decision-Paths-Algorithmic-Affordance

Decision Paths

Tuning algoritmic parameters

13. Decision Paths

By showing how decision paths work (for example visualizing the layers in a recommender system) users can get an idea about why they get recommended certain things. The decision paths can be made adjustable by the user to tune their recommendations.

What can designers do with this pattern?

Example of peer pickers

Examples?

Still to be found in the wild.

Input or output?

Input

How do users control the algorithm?

Recommendation paths in itself do not offer the user control. Imaginable is that users can change paths. As has been implemented in some experimental systems.

How do users understand the control?

Users can gain a notion of how recommendations are composed.

Some examples of use

Relevant theories

Please let us know if you have a related reference with the scientific research.

Comments & Talks

React on this algoritmic affordance and get in touch with the publisher over this algoritmic affordance Koen van Turnhout.

Add
Comment

woc1bk

🎁 Get free iPhone 14 Pro Max: http://hammonddesignweb.com/uploads/go.php 🎁 hs=460d96ed76e3d62acc4a8ef465602712*

cmduq2

nn5vwi

Related Patterns

Related algoritmic affordance pattern in this library are listed below.

Peer-Picker-algorithmic-affordances

Peer Picker

Tuning algoritmic parameters

Data-Toggle-Icon

Data Toggles

Tuning algoritmic parameters