Research and Application of Personalized News Recommendation Algorithm Based on Deep Learning
Li Ping Gao, Jie Zhao · 2024
In order to solve the problem of information overload caused by users' demand for information in the information age, the author proposes a personalized news recommendation algorithm based on deep learning. Integrate attention mechanism into NCF. Specifically, it is based on the assumption that different cross features have varying degrees of impact on the results, considering the varying degrees of impact of different features on the model, adjusting the weights of features, and optimizing losses. The experimental results show that the attribute based NCF algorithm has achieved good results on both samples, far exceeding several popular algorithms currently. The HR of video websites are 0.747, 0.453, 0.890, and 0.563, respectively. In the same experimental environment and data set, the test results show that compared with NCF, the performance indicators of this algorithm have been improved. The optimization of this algorithm has significantly improved the accuracy of news recommendations, while also enhancing the user experience, enabling users to more quickly find content of interest amidst a vast amount of information.