Intelligent Recommendation Algorithm Combining RNN and Knowledge Graph

• Published in: Journal of applied mathematics Vol. 2022; pp. 1 - 11
• Main Authors: Zeng, Fengsheng, Wang, Qin
• Format: Journal Article
• Language: English
• Published: New York Hindawi 21.12.2022; John Wiley & Sons, Inc; Wiley
• Subjects: Access to information; Accuracy; Algorithms; Big data; Deep learning; Earthquakes; Knowledge; Knowledge representation; Medical research; Methods; Neural networks; Privacy, Right of; Recurrent neural networks; Taiwan
• ISSN: 1110-757X; 1687-0042; 1687-0042
• DOI: 10.1155/2022/7323560

With the continuous application and development of big data and algorithm technology, intelligent recommendation algorithms are gradually affecting all aspects of people’s daily life. The impact of smart recommendation algorithm has both advantages and disadvantages; it can facilitate people’s life, but also exists at the same time the invasion of privacy, information cocoon, and other problems. How to optimize intelligent recommendation algorithms to serve the society more safely and efficiently becomes a problem that needs to be solved nowadays. We propose an intelligent recommendation algorithm combining recurrent neural network (RNN) and knowledge graph (KG) and analyze and demonstrate its performance by building models and experiments. The results show that among the five different recommendation models, the intelligent recommendation algorithm model combining RNN and knowledge graph has the highest AUC and ACC values in the Book-Crossing and MovieLens-1M. At the same time, the algorithm’s rating prediction error values are small (less than 2%) in extracting different users’ ratings for different books. In addition, the intelligent recommendation algorithm combining RNN and knowledge graph has the lowest RMSE and MAE values in the comparison of three different recommendation algorithms, indicating that it has better performance and stability, which is important for the improvement of user recommendation effect.