Abstract
Big data has been developed for nearly a decade, and the information data on the network is exploding. Facing the complex and massive data, it is difficult for people to get the demanded information quickly, and the recommendation algorithm with its characteristics becomes one of the important methods to solve the massive data overload problem at this stage. In particular, the rise of the e-commerce industry has promoted the development of recommendation algorithms. Traditional, single recommendation algorithms often have problems such as cold start, data sparsity, and long-tail items. The hybrid recommendation algorithms at this stage can effectively avoid some of the drawbacks caused by a single algorithm. To address the current problems, this paper makes up for the shortcomings of a single collaborative model by proposing a hybrid recommendation algorithm based on deep learning IA-CN. The algorithm first uses an integrated strategy to fuse user-based and item-based collaborative filtering algorithms to generalize and classify the output results. Then deeper and more abstract nonlinear interactions between users and items are captured by improved deep learning techniques. Finally, we designed experiments to validate the algorithm. The experiments are compared with the benchmark algorithm on (Amazon item rating dataset), and the results show that the IA-CN algorithm proposed in this paper has better performance in rating prediction on the test dataset.
Highlights
With the continuous development and maturity of big data, cloud computing, and other technologies, this has greatly enriched our production and life
En, based on the fusion model, the deeper and abstract nonlinear interaction relationship between users and items is captured through deep learning technology, which can effectively solve the cold start and long tail items problems that exist in traditional algorithms
For the processing of fine ranking, we propose a new deep network-convolutional neural networks (CNN) improved network based on the attention mechanism (IA-CN), which is used for user’s real-time recommendation
Summary
With the continuous development and maturity of big data, cloud computing, and other technologies, this has greatly enriched our production and life. Recommendation algorithm is one of the effective means to solve the above problems It can be modeled by analyzing users’ browsing history, user-item rating information, and other information such as users’ preferences, to discover some potential personalized needs of users. En, based on the fusion model, the deeper and abstract nonlinear interaction relationship between users and items is captured through deep learning technology, which can effectively solve the cold start and long tail items problems that exist in traditional algorithms. Salakhutdinov et al [13] first introduced deep learning to learn the implicit factors of users and items in recommender systems and proposed a restricted Boltzmann machine- (RBM-) based collaborative filtering algorithm. Deep learning techniques have made certain achievements in the field of recommendation algorithms, there is still a great deal of improvement in the application of deep learning techniques in the field of personalized recommendation algorithms, which presents new challenges and opportunities for related research work
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