Multi-Graph Heterogeneous Interaction Fusion for Social Recommendation

With the rapid development of online social recommendation system, substantial methods have been proposed. Unlike traditional recommendation system, social recommendation performs by integrating social relationship features, where there are two major challenges, i.e., early summarization and data sparsity. Thus far, they have not been solved effectively. In this article, we propose a novel social recommendation approach, namely Multi-Graph Heterogeneous Interaction Fusion (MG-HIF), to solve these two problems. Our basic idea is to fuse heterogeneous interaction features from multi-graphs, i.e., user–item bipartite graph and social relation network, to improve the vertex representation learning. A meta-path cross-fusion model is proposed to fuse multi-hop heterogeneous interaction features via discrete cross-correlations. Based on that, a social relation GAN is developed to explore latent friendships of each user. We further fuse representations from two graphs by a novel multi-graph information fusion strategy with attention mechanism. To the best of our knowledge, this is the first work to combine meta-path with social relation representation. To evaluate the performance of MG-HIF, we compare MG-HIF with seven states of the art over four benchmark datasets. The experimental results show that MG-HIF achieves better performance.

Back-Propagation Neural Network and ARIMA Algorithm for GDP Trend Analysis

GDP (gross domestic product) is a key indicator for assessing a country’s or region’s macroeconomic situation, as well as a foundation for the government to develop economic development strategies and macroeconomic policies. Currently, the majority of methods for forecasting GDP are linear methods, which only take into account the linear factors that affect GDP. GDP (gross domestic product) is widely regarded as the most accurate indicator of a country’s economic health. GDP not only reflects a country’s economic development over time but can also reflect its national strength and wealth. As a result, the GDP trend forecast partially reflects China’s transformation and future development. The time series ARIMA (Autoregressive Integrated Moving Average) model and the BPNN (BP neural network) model are combined in this article to create the ARIMA-BPNN fusion prediction model. The predicted values of the two models were then weighted averaged to obtain the predicted values of the linear part of the improved fusion model. To get the predicted values of the improved fusion model, we weighted average the residual parts of the two models, predict the nonlinear residual with BPNN, and add the predicted values of the two parts. It is applied to the actual GDP forecast in H province from 2019 to 2022, and the actual forecast verifies the effectiveness of the fusion forecast model in the actual forecast.

Research on the Effect of Online Marketing Based on Multimodel Fusion and Artificial Intelligence in the Context of Big Data

Traditional online marketing methods use a single model to predict the advertising conversion rate, but the prediction results are not accurate, and users are not satisfied with the recommendation results. Therefore, this paper proposes an online marketing method based on multimodel fusion and artificial intelligence algorithms under the background of big data. First, it introduces big data technology and analyzes the characteristics of network advertising marketing model (RTB). Second, combined with multitask learning and fusion technology to improve the single model in advertising conversion rate prediction effect, prediction results to further improve the accuracy of results. Then, tF-IDF technology in artificial intelligence algorithm is used to measure the importance of advertising words in online marketing and calculate the contribution degree. Finally, according to XGBoost technology, the multitask fusion model of online marketing effect is classified. Experiments are used to analyze the effect of online marketing. Experimental results show that the proposed method can improve the accuracy of advertising conversion rate prediction and online sales of goods.

Offensive-Language Detection on Multi-Semantic Fusion Based on Data Augmentation

The rapid identification of offensive language in social media is of great significance for preventing viral spread and reducing the spread of malicious information, such as cyberbullying and content related to self-harm. In existing research, the public datasets of offensive language are small; the label quality is uneven; and the performance of the pre-trained models is not satisfactory. To overcome these problems, we proposed a multi-semantic fusion model based on data augmentation (MSF). Data augmentation was carried out by back translation so that it reduced the impact of too-small datasets on performance. At the same time, we used a novel fusion mechanism that combines word-level semantic features and n-grams character features. The experimental results on the two datasets showed that the model proposed in this study can effectively extract the semantic information of offensive language and achieve state-of-the-art performance on both datasets.

Investigation into Cervical Spine Biomechanics Following Single, Multilevel and Hybrid Disc Replacement Surgery with Dynamic Cervical Implant and Fusion: A Finite Element Study

Cervical fusion has been a standard procedure for treating abnormalities associated with the cervical spine. However, the reliability of anterior cervical discectomy and fusion (ACDF) has become arguable due to its adverse effects on the biomechanics of adjacent segments. One of the drawbacks associated with ACDF is adjacent segment degeneration (ASD), which has served as the base for the development of dynamic stabilization systems (DSS) and total disc replacement (TDR) devices for cervical spine. However, the hybrid surgical technique has also gained popularity recently, but its effect on the biomechanics of cervical spine is not well researched. Thus, the objective of this FE study was to draw a comparison among single-level, bi-level, and hybrid surgery with dynamic cervical implants (DCIs) with traditional fusion. Reductions in the range of motion (ROM) for all the implanted models were observed for all the motions except extension, compared to for the intact model. The maximum increase in the ROM of 42% was observed at segments C5–C6 in the hybrid DCI model. The maximum increase in the adjacent segment’s ROM of 8.7% was observed in the multilevel fusion model. The maximum von Mises stress in the implant was highest for the multilevel DCI model. Our study also showed that the shape of the DCI permitted flexion/extension relatively more compared to lateral bending and axial rotation.

Improving the Accuracy of Groundwater Storage Estimates Based on Groundwater Weighted Fusion Model

It is an effective measure to estimate groundwater storage anomalies (GWSA) by combining Gravity Recovery and Climate Experiment (GRACE) data and hydrological models. However, GWSA results based on a single hydrological model and GRACE data may have greater uncertainties, and it is difficult to verify in some regions where in situ groundwater-level measurements are limited. First, to solve this problem, a groundwater weighted fusion model (GWFM) is presented, based on the extended triple collocation (ETC) method. Second, the Shiyang River Basin (SYRB) is taken as an example, and in situ groundwater-level measurements are used to evaluate the performance of the GWFM. The comparison indicates that the correlation coefficient (CC) and Nash-Sutcliffe efficiency coefficient (NSE) are increased by 9–40% and 23–657%, respectively, relative to the original results. Moreover, the root mean squared error (RMSE) is reduced by 9–28%, which verifies the superiority of the GWFM. Third, the spatiotemporal distribution and influencing factors of GWSA in the Hexi Corridor (HC) are comprehensively analyzed during the period between 2003 and 2016. The results show that GWSA decline, with a trend of −2.37 ± 0.38 mm/yr from 2003 to 2010, and the downward trend after 2011 (−0.46 ± 1.35 mm/yr) slow down significantly compared to 2003–2010. The spatial distribution obtained by the GWFM is more reliable compared to the arithmetic average results, and GWFM-based GWSA fully retain the advantages of different models, especially in the southeastern part of the SYRB. Additionally, a simple index is used to evaluate the contributions of climatic factors and human factors to groundwater storage (GWS) in the HC and its different subregions. The index indicates that climate factors occupy a dominant position in the SLRB and SYRB, while human factors have a significant impact on GWS in the Heihe River Basin (HRB). This study can provide suggestions for the management and assessments of groundwater resources in some arid regions.