Churn-Aware Recommendation Planning under Aggregated Preference Feedback
This addresses the challenge of personalizing recommendations while preventing user churn in privacy-aware settings, though it is incremental as it builds on existing sequential decision-making models.
The paper tackles the problem of sequential recommendation planning under privacy constraints where only aggregated user preference data is available, and it shows that optimal policies converge to exploitation quickly, with experiments on synthetic and MovieLens data demonstrating performance gains over existing methods like SARSOP, especially in scenarios with many user types.
We study a sequential decision-making problem motivated by recent regulatory and technological shifts that limit access to individual user data in recommender systems (RSs), leaving only population-level preference information. This privacy-aware setting poses fundamental challenges in planning under uncertainty: Effective personalization requires exploration to infer user preferences, yet unsatisfactory recommendations risk immediate user churn. To address this, we introduce the Rec-APC model, in which an anonymous user is drawn from a known prior over latent user types (e.g., personas or clusters), and the decision-maker sequentially selects items to recommend. Feedback is binary -- positive responses refine the posterior via Bayesian updates, while negative responses result in the termination of the session. We prove that optimal policies converge to pure exploitation in finite time and propose a branch-and-bound algorithm to efficiently compute them. Experiments on synthetic and MovieLens data confirm rapid convergence and demonstrate that our method outperforms the POMDP solver SARSOP, particularly when the number of user types is large or comparable to the number of content categories. Our results highlight the applicability of this approach and inspire new ways to improve decision-making under the constraints imposed by aggregated preference data.