人工智能驱动新闻个性化推荐算法优化与效果评估 |
| 陈逸斐 孔祥通 |
| 1 山东师范大学，山东济南，250000；2 曲阜远东职业技术学院，山东曲阜，273100 |
摘要：随着互联网信息的爆炸式增长，用户面临着信息过载的问题。个性化新闻推荐系统应运而生，旨在帮助用户从海量信息中快速准确地获取感兴趣的新闻内容。然而，传统的推荐算法在处理复杂的用户行为和新闻内容时存在局限性。本文深入研究了基于人工智能的新闻个性化推荐算法，重点探讨了基于深度学习的模型构建、基于强化学习的策略优化、冷启动问题与多样性优化等方面。通过理论分析和实验验证，我们提出了一系列优化方案，并对推荐效果进行了全面评估。实验结果表明，本文提出的算法在准确率、召回率和用户满意度等方面均取得了显著提升，为新闻个性化推荐的进一步发展提供了有益参考。
关健词：个性化推荐；新闻推荐；深度学习；强化学习；冷启动；多样性
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Optimization and Effect Evaluation of AI-Powered News Personalization Recommendation Algorithms |
Yifei Chen Xiangtong Kong
1.Shandong Normal University, Jinan, Shandong, 250000, China;2.Qufu Far East Vocational and Technical College, Qufu, Shandong, 273100, China
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Abstract：With the explosive growth of internet information, users face the issue of information overload. Personalized newsrecommendation systems have emerged to help users quickly and accurately obtain news content of interest from vast amountsof information. However, traditional recommendation algorithms have limitations in handling complex user behaviors and newscontent. This paper conducts an in-depth study on AI-based personalized news recommendation algorithms, focusing on aspectssuch as deep learning-based model construction, reinforcement learning-based strategy optimization, cold-start problems, anddiversity optimization. Through theoretical analysis and experimental validation, we propose a series of optimization solutionsand conduct a comprehensive evaluation of the recommendation effectiveness. The experimental results demonstrate that theproposed algorithm achieves significant improvements in accuracy, recall rate, and user satisfaction, providing valuable insightsfor further advancements in personalized news recommendation.
Keywords : Personalized recommendation；News recommendation；Deep learning；Reinforcement learning；Cold start；Diversity
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