User dynamic topology-information-based matrix factorization for e-government recommendation

Abstract

Today, there is a growing tendency to integrate all public services on e-government websites, a trend that increases the risk of information overload. Matrix factorization (MF)-based recommendation models can effectively predict the user preference for an item, thus demonstrating their superiority in alleviating information overload problems. However, factors such as poor latent representations and the linear interaction function limit the further performance of these models To address these issues, we propose a novel method named user dynamic topology-information-based matrix factorization (User-DTMF). User-DTMF contains two designed modules: the dynamic topology feature learning module and the interactive preference learning module. The former first constructs the user dynamic topology sequence based on user historical behaviors. A convolutional neural network (CNN) is then used to extract features from the sequence. To obtain the final representations of users and items, the latter module uses MF and a fully connected (FC) neural network to model the dynamic topology features and static user-item interactive information. Next, another FC layer, which can be regarded as a nonlinear interaction function, is used to predict the probability of a user clicking on an item. Comprehensive experiments on a real dataset show that User-DTMF outperforms the best benchmark method by a margin of 1.68% and 3.05% in terms of Recall@10 and NDCG@10, respectively.