Dynamic-K: Recommendations That Know When to Stop
Most apps show a fixed number of “top” items—say 10 movies or 20 products—assuming there are always enough good options. But that’s not always true: sometimes there are few relevant items, or some users are extra picky. The result? Filler recommendations.
Dynamic-K flips the script. Instead of always showing a fixed N, it learns how many items should be shown for each user and situation. The system jointly learns to: (1) rank items by likely interest, and (2) set a personalized decision boundary that separates relevant from irrelevant. The number of recommendations (K) becomes “how many items clear your bar.”
- Fewer junk suggestions when choices are scarce
- More trust and better UX for selective users
- Stronger results than fixed-length lists in tests on two datasets
The authors extend popular ranking models to “Dynamic-K” versions and report consistent gains. Paper: http://arxiv.org/abs/2012.13569v1
Paper: http://arxiv.org/abs/2012.13569v1
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