Preference-based learning for news headline recommendation

2025-05-31

AI Summary

This paper addresses the problem of optimizing user engagement in French news headline recommendation under noisy contextual interference. We propose a preference-learning-based contextual bandit framework that eliminates the need for explicit exploration—demonstrating theoretically and empirically that implicit exploration suffices for accurate click-through rate (CTR) prediction and policy optimization in realistic noisy settings. Our method integrates online interactive data collection, multi-strategy A/B testing, and lightweight preference modeling. Notably, we conduct the first systematic evaluation of machine translation quality on cross-lingual news engagement prediction. Experimental results show a +12.3% improvement in CTR prediction accuracy, validating the effectiveness of our exploration-free design. The approach offers a scalable, low-overhead paradigm for resource-constrained industrial news recommendation systems, bridging the gap between theoretical bandit learning and practical deployment constraints.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Optimizing news headline recommendations using preference-based learning
• Exploring translation impact on engagement predictions in recommendations
• Assessing interactive strategies for user engagement during data collection

Innovation

• Preference-based learning for headline recommendation
• Contextual bandit setting for recommender agent
• Exploration impact on engagement predictions