Multi-view knowledge representation learning for personalized news recommendation - Chao Chang; Feiyi Tang; Peng Yang; Jingui Zhang; Jingxuan Huang; Junxian Li; Zhenjun Li (Scientific Reports)
Abstract 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.