arXiv:2606. 15146v1 Announce Type: new Abstract: Stimulated word-of-mouth is a strategy that promotes information sharing through prompts or incentives.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2510. 19119v2 Announce Type: replace Abstract: In networked environments, it is common for users to share recommendations about content, products, services, and possible courses of action.
arXiv:2501. 07761v2 Announce Type: replace-cross Abstract: Increasingly, recommender systems are tasked with improving users' long-term satisfaction.
arXiv:2607. 09015v1 Announce Type: cross Abstract: We study contextual bandit problems with correlated arms and access to surrogate reward signals produced by a machine learning model, motivated by applications such as large language model (LLM) routing.
arXiv:2609.37800v1 Announce Type: cross Abstract: Many recommender services repeatedly encounter cold-start cohorts, where new users arrive with little or no interaction history. This creates two cha...
The paper introduces Contextual Scalarisation Thompson Sampler (CSTS), a multi‑objective contextual bandit algorithm that learns to weight competing objectives based on observed context. It addresses the need for adaptable decision‑making in public media, where goals such as audience reach, cultural values, and operational constraints must be balanced. Experiments on Radio Télévision Suisse data demonstrate that CSTS improves contextual relevance and aligns more closely with expert curation than fixed‑weight or standard bandit methods.
arXiv:2608. 03382v1 Announce Type: cross Abstract: Multi-armed bandit algorithms, especially Thompson sampling, are widely used in online recommendation.