Intelligent Recommendation System Based on the Infusion Algorithms with Deep Learning, Attention Network and Clustering

The creation and use of big data have driven the intelligent development of e-commerce. The information generated in e-commerce provides a good means to analyze the behavior of users. How to use this information to give customer recommendations, improve the accuracy of recommendations and protect information security is a topic worth studying. For improving the accuracy of recommendations, analysis of users and tagging of resources are key. The current popular session recommendation algorithms face many problems, such as user interest drift which is difficult to be handled by these algorithms, thus affecting the recommendation accuracy. Based on these problems, this paper proposes a recommendation model based on deep learning, applies it to the clustering analysis of user tagging system, and designs a personalized recommendation algorithm for the tagging system. The model proposed in this paper can effectively analyze not only the interests exhibited by users in the current session, but also their potential long-term interests. By comparing the different performances of different datasets, the experimental results of this paper show that the proposed algorithmic model in this paper helps to dig the interests of different users, thus improving the quality of the recommendation system.