Personalized online shopping recommendation algorithm and practice based on deep Q-network

With the rapid development of e-commerce and the increasing diversity of user demands, personalized recommendations on online shopping platforms have become an important direction of research in the field of e-commerce, which is extremely important for promoting the intelligent transformation of the e-commerce industry. Personalized recommendations on online shopping platforms directly promote the growth of e-commerce sales by improving user experience and satisfaction; at the same time, through the application of intelligent recommendation technology, it accelerates the technological innovation of the e-commerce industry and the changes in the competitive landscape. However, current personalized recommendation algorithms still have problems such as poor algorithm performance, long iteration time, and low recommendation precision. To better achieve personalized recommendations for online shopping, this article applies deep Q-network (DQN) to explore personalized recommendation algorithms in depth. In the article, data is first collected and preprocessed through data augmentation, and based on the deep Q-network, a user–product interaction matrix is constructed. Afterward, based on user behavior characteristic data, user behavior modeling is implemented. Then a DQN-based learning architecture is constructed by combining long-term interests, short-term interests, and prediction modules, and a small batch gradient descent method is used to train the function. Finally, this article evaluates the performance of the algorithm. The research results show that when the length of the recommendation list is 1, the accuracy, recall, and mean average precision of the DQN algorithm are 0.924, 0.875, and 0.901, respectively. The recommendation algorithm based on DQN achieves good results in indicators of recommendation accuracy, recall, and mean average precision. In this article, DQN is applied, and the online shopping recommendation system is optimized by combining deep learning and reinforcement learning, significantly improving the quality and efficiency of recommendations and promoting the intelligent development of the e-commerce industry.