Preference-based learning for news headline recommendation

TL;DR

This paper investigates preference-based learning for news headline recommendation, demonstrating that simpler strategies can be effective in noisy contexts and exploring the impact of translation and interactive strategies on user engagement.

Contribution

It introduces a preference-based learning approach for news recommendation using a contextual bandit model with real-world data, highlighting the effectiveness of simpler strategies in noisy environments.

Findings

Explicit exploration may be unnecessary with noisy contexts

Translation impacts engagement prediction accuracy

Different interactive strategies influence user engagement

Abstract

This study explores strategies for optimizing news headline recommendations through preference-based learning. Using real-world data of user interactions with French-language online news posts, we learn a headline recommender agent under a contextual bandit setting. This allows us to explore the impact of translation on engagement predictions, as well as the benefits of different interactive strategies on user engagement during data collection. Our results show that explicit exploration may not be required in the presence of noisy contexts, opening the door to simpler but efficient strategies in practice.