Sequential recommendation tasks predict items to be interacted at the next moment according to users’ historical behavior sequences. A large number of studies have shown that accuracy is not the only evaluation metric in the sequential recommendation tasks. Diversity can measure homogeneity of items to capture the changes of users’ interests in the sequences. Integrating multiple interests of users has become the focus of current research. The existing multi-interest sequential recommendation methods adopt the method of self-attention, but it is based on the self-attention of transformer, which lacks the consideration of the correlation between different samples. Therefore, we propose an Enhanced Attention (EA) framework, which is based on two linear layers and two norm layers. Compared with self-attention, it not only reduces the high computational complexity, but also obtains the correlation between different samples. Multi-head mechanism is also applied to EA framework. We conduct experiments for the sequential recommendation on three real-world datasets, Amazon, Taobao and MovieLens-1M. The experimental results show that the EA framework is significantly improved compared with the current state-of-the-art models.

Users’ interests are often diverse and multi-grained, with their underlying intents even more so. Effectively capturing users’ interests and uncovering the relationships between diverse interests are key to news recommendation. Meanwhile, diversity is an important metric for evaluating news recommendation algorithms, as users tend to reject excessive homogeneous information in their recommendation lists. However, recommendation models themselves lack diversity awareness, making it challenging to achieve a good balance between the accuracy and diversity of news recommendations. In this paper, we propose a news recommendation algorithm that achieves good performance in both accuracy and diversity. Unlike most existing works that solely optimize accuracy or employ more features to meet diversity, the proposed algorithm leverages the diversity-aware capability of the model. First, we introduce an augmented user model to fully capture user intent and the behavioral guidance they might undergo as a result. Specifically, we focus on the relationship between the original clicked news and the augmented clicked news. Moreover, we propose an effective adversarial training method for diversity (AT4D), which is a pluggable component that can enhance both the accuracy and diversity of news recommendation results. Extensive experiments on real-world datasets confirm the efficacy of the proposed algorithm in improving both the accuracy and diversity of news recommendations.

With the growth of urban population, a series of urban problems have emerged, and how to speed up smart city construction has received extensive attention. Remote sensing images have the advantages of wide spatial coverage and rich information, and it is suitable for use as research data for smart cities. However, due to limitations in the imaging sensor conditions and complex weather, remote sensing images face the problems of insufficient resolution and cloud occlusion, which cannot meet the resolution requirements of smart city tasks. The remote sensing image super-resolution (SR) technique can improve the details and texture information without upgrading the imaging sensor system, which becomes a feasible solution for the above problems. In this paper, we propose a novel remote sensing image super-resolution method which leverages the texture features from internal and external references to help with SR reconstruction. We introduce the transformer attention mechanism to select and extract parts of texture features with high reference values to ensure that the network is lightweight, effective, and easier to deploy on edge computing devices. In addition, our network can automatically learn and adjust the alignment angles and scales of texture features for better SR results. Extensive comparison experiments show that our proposed method achieves superior performance compared with several state-of-the-art SR methods. In addition, we also evaluate the application value of our proposed SR method in urban region function recognition in smart cities. The dataset used in this task is low-quality. The comparative experiment between the original dataset and the SR dataset generated by our proposed SR method indicates that our method can effectively improve the recognition accuracy.

Based on Agenda-Setting Theory, Media Agenda Synergy (MAS) can enhance the communication effectiveness of public issues (e.g., climate change, social justice, and public health) through the information resonance and agenda complementarity among cross-media platforms, thus reconstructing the public perception. In this paper, we focus on the dynamic impact of cross-media agenda synergy on public agenda intensity and innovatively propose a “HMM-Granger” hybrid modeling framework for Media Agenda Synergy: Firstly, we quantify the causal weights of agenda shifting based on the deconstruction of the nonlinear time-series dependence of multisource media data by using LSTM neural networks. Secondly, the state transfer probability matrix of the Hidden Markov Model reveals the dual paths of “explicit collaboration” (e.g., issue resonance) and “implicit competition” (e.g., agenda masking) in media agenda coordination. The results of this study show that the Agenda Synergy between mainstream media and social media during major events can generate an Agenda Multiplier Effect, resulting in a significant increase in the intensity of the public agenda. This study provides a computable theoretical paradigm for Inter-Media Agenda Network modeling and data-driven decision support for optimizing opinion guidance strategies.

At present, the media industry is in the rapid development of convergence media, and is undergoing the development process of gradually evolving from full coverage of new media to full integration of media. Blockchain is a distributed database technology, with decentralized, immutable, traceable and other characteristics. The convergence media ecology built on blockchain technology can delegate power to ordinary nodes in the ecology, which is expected to have a profound impact and change on the existing convergence media business model, content creation method and industry structure. Based on the special attributes of the media industry, the blockchain convergence media ecology uses the form of alliance chain in the early stage of establishment. The existing blockchain node management mechanism is universal and does not adapt to the characteristics of the media industry. In order to ensure the basic operation and maintenance of the blockchain convergence media ecology, this paper defines the participation and credit degree system of the ecological node. On this basis, a Node dynamic access and exit (NDAE) operation and maintenance mechanism based on node interaction and node integrity was established. Simulation experiments show that the NDAE mechanism can better handle the access, exit and forced exit of nodes, which ensures the security of the blockchain convergence media ecology to a certain extent.