Chang Chao, Tang Feiyi, Yang Peng, Zhang Jingui, Huang Jingxuan, Li Junxian, Li Zhenjun
School of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou, 511483, China.
School of Computer Science, South China Normal University, Guangzhou, 510631, China.
Sci Rep. 2025 Jan 7;15(1):1152. doi: 10.1038/s41598-025-85166-0.
In the rapidly evolving field of personalized news recommendation, capturing and effectively utilizing user interests remains a significant challenge due to the vast diversity and dynamic nature of user interactions with news content. Existing recommendation models often fail to fully integrate candidate news items into user interest modeling, which can result in suboptimal recommendation accuracy and relevance. This limitation stems from their insufficient ability to jointly consider user history and the characteristics of candidate news items in the modeling process. To address this challenges, we propose the Multi-view Knowledge Representation Learning (MKRL) framework for personalized news recommendation, which leverages a multi-view news encoder and candidate-aware attention mechanisms to enhance user interest modeling. Unlike traditional methods, MKRL incorporates candidate news articles directly into the user interest modeling process, enabling the model to better understand and predict user preferences based on both historical behavior and potential new content. This is achieved through a sophisticated architecture that blends a multi-view news encoder and candidate-aware attention mechanisms, which together capture a more holistic and dynamic view of user interests. The MKRL framework innovatively integrates convolutional neural networks with multi-head attention modules to capture intricate contextual information from both user history and candidate news, allowing the model to recognize fine-grained patterns. The multi-head attention dynamically weighs user interactions and candidate news based on relevance, enhancing recommendation accuracy. Additionally, MKRL's multi-view approach represents news from different perspectives, enabling richer and more personalized recommendations. Extensive experiments on three real-world datasets demonstrate that our proposed framework outperforms state-of-the-art baselines in recommendation accuracy, validating its effectiveness.
在快速发展的个性化新闻推荐领域，由于用户与新闻内容交互的巨大多样性和动态性，捕捉并有效利用用户兴趣仍然是一项重大挑战。现有的推荐模型往往未能将候选新闻条目充分整合到用户兴趣建模中，这可能导致推荐准确性和相关性欠佳。这一局限性源于它们在建模过程中联合考虑用户历史和候选新闻条目的特征的能力不足。为应对这一挑战，我们提出了用于个性化新闻推荐的多视图知识表示学习（MKRL）框架，该框架利用多视图新闻编码器和候选感知注意力机制来增强用户兴趣建模。与传统方法不同，MKRL将候选新闻文章直接纳入用户兴趣建模过程，使模型能够基于历史行为和潜在的新内容更好地理解和预测用户偏好。这是通过一种复杂的架构实现的，该架构融合了多视图新闻编码器和候选感知注意力机制，共同捕捉用户兴趣更全面、动态的视图。MKRL框架创新性地将卷积神经网络与多头注意力模块集成，以从用户历史和候选新闻中捕捉复杂的上下文信息，使模型能够识别细粒度模式。多头注意力根据相关性动态权衡用户交互和候选新闻，提高推荐准确性。此外，MKRL的多视图方法从不同角度表示新闻，实现更丰富、个性化的推荐。在三个真实世界数据集上进行的大量实验表明，我们提出的框架在推荐准确性方面优于现有最先进的基线，验证了其有效性。
