Collaborative Filtering under Model Uncertainty
This work addresses the problem of model reliability for users in recommender systems, but it is incremental as it builds on existing definitions and shows limited variation in outcomes.
The authors investigated how model uncertainty in collaborative filtering affects item availability and recourse in recommender systems, finding that most parameter variations produced similar results with only some cases showing significant differences.
In their work, Dean, Rich, and Recht create a model to research recourse and availability of items in a recommender system. We used the definition of predictive multiplicity by Marx, Pin Calmon, and Ustun to examine different variations of this model, using different values for two model parameters. Pairwise comparison of their models show, that most of these models produce very similar results in terms of discrepancy and ambiguity for the availability and only in some cases the availability sets differ significantly.