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Found 7 papers, 3 papers with code
Initializing Services in Interactive ML Systems for Diverse Users
(ii) Suboptimal Local Solutions: The total loss (sum of loss functions across all users and all services) landscape is not convex even if the individual losses on a single service are convex, making it likely for the learning dynamics to get stuck in local minima.
Private Matrix Factorization with Public Item Features
It can be applied to different types of public item data, including: (1) categorical item features; (2) item-item similarities learned from public sources; and (3) publicly available user feedback.
Delayed and Indirect Impacts of Link Recommendations
Moreover, we show that the effects of recommendations can persist in networks, in part due to their indirect impacts on natural dynamics even after recommendations are turned off.
Towards Psychologically-Grounded Dynamic Preference Models
Second, we discuss implications of dynamic preference models for recommendation systems evaluation and design.
Emergent specialization from participation dynamics and multi-learner retraining
Numerous online services are data-driven: the behavior of users affects the system's parameters, and the system's parameters affect the users' experience of the service, which in turn affects the way users may interact with the system.
Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability
In this work, we consider how preference models in interactive recommendation systems determine the availability of content and users' opportunities for discovery.
Do Offline Metrics Predict Online Performance in Recommender Systems?
We observe that offline metrics are correlated with online performance over a range of environments.
