Multi-behavioral recommendation model based on dual neural networks and contrast learning.

In order to capture the complex dependencies between users and items in a recommender system and to alleviate the smoothing problem caused by the aggregation of multi-layer neighborhood information, a multi-behavior recommendation model (DNCLR) based on dual neural networks and contrast learning is proposed. In this paper, the complex dependencies between behaviors are divided into feature correlation and temporal correlation. First, we set up a personalized behavior vector for users and use a graph-convolution network to learn the features of users and items under different behaviors, and we then combine the features of self-attention mechanism to learn the correlation between behaviors. The multi-behavior interaction sequence of the user is input into the recurrent neural network, and the temporal correlation between the behaviors is captured by combining the attention mechanism. The contrast learning is introduced based on the double neural network. In the graph convolution network layer, the distances between users and similar users and between users and their preference items are shortened, and the distance between users and their short-term preference is shortened in the circular neural network layer. Finally, the personalized behavior vector is integrated into the prediction layer to obtain more accurate user, behavior and item characteristics. Compared with the sub-optimal model, the HR@10 on Yelp, ML20M and Tmall real datasets are improved by 2.5%, 0.3% and 4%, respectively. The experimental results show that the proposed model can effectively improve the recommendation accuracy compared with the existing methods.

Recommendation model based on intention decomposition and heterogeneous information fusion.

In order to solve the problem of timeliness of user and item interaction intention and the noise caused by heterogeneous information fusion, a recommendation model based on intention decomposition and heterogeneous information fusion (IDHIF) is proposed. First, the intention of the recently interacting items and the users of the recently interacting candidate items is decomposed, and the short feature representation of users and items is mined through long-short term memory and attention mechanism. Then, based on the method of heterogeneous information fusion, the interactive features of users and items are mined on the user-item interaction graph, the social features of users are mined on the social graph, and the content features of the item are mined on the knowledge graph. Different feature vectors are projected into the same feature space through heterogeneous information fusion, and the long feature representation of users and items is obtained through splicing and multi-layer perceptron. The final representation of users and items is obtained by combining short feature representation and long feature representation. Compared with the baseline model, the AUC on the Last.FM and Movielens-1M datasets increased by 1.83 and 4.03 percentage points, respectively, the F1 increased by 1.28 and 1.58 percentage points, and the Recall@20 increased by 3.96 and 2.90 percentage points. The model proposed in this paper can better model the features of users and items, thus enriching the vector representation of users and items, and improving the recommendation efficiency.

Process intensification of continuous-flow seATRP by a sonicated multi-reactor setup.

Simplified electrochemically mediated atom transfer radical polymerization (seATRP) is a versatile technique for synthesizing polymers with precise control and complex architecture. Continuous-flow seATRP has recently been realized by using a sonicated microreactor but still faces limitations such as relatively low conversion and difficulties in synthesizing polymers with high molecular weight. Herein, a novel multi-reactor setup is demonstrated. By tuning the currents applied to different reaction stages in the setup, 90% conversion can be achieved while maintaining relatively low dispersity (<1.35). Meanwhile, the unique design enables a wider processing window for sonication due to greater viscous attenuation in the second reactor, thus largely addressing the problem associated with high viscosity during the synthesis of high molecular weight polymers. The developed setup also offers an alternative strategy for future scale-up of continuous-flow seATRP.

A Social Recommendation Model Based on Basic Spatial Mapping and Bilateral Generative Adversarial Networks.

Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data. Therefore, how to effectively fuse interaction information and social information becomes a hot research topic in social recommendation, and how to mine and exploit the heterogeneous information in the interaction and social space becomes the key to improving recommendation performance. In this paper, we propose a social recommendation model based on basic spatial mapping and bilateral generative adversarial networks (MBSGAN). First, we propose to map the base space to the interaction and social space, respectively, in order to overcome the issue of heterogeneous information fusion in two spaces. Then, we construct bilateral generative adversarial networks in both interaction space and social space. Specifically, two generators are used to select candidate samples that are most similar to user feature vectors, and two discriminators are adopted to distinguish candidate samples from high-quality positive and negative examples obtained from popularity sampling, so as to learn complex information in the two spaces. Finally, the effectiveness of the proposed MBSGAN model is verified by comparing it with both eight social recommendation models and six models based on generative adversarial networks on four public datasets, Douban, FilmTrust, Ciao, and Epinions.

Recommendation model based on generative adversarial network and social reconstruction.

Social relations can effectively alleviate the data sparsity problem in recommendation, but how to make effective use of social relations is a difficulty. However, the existing social recommendation models have two deficiencies. First, these models assume that social relations are applicable to various interaction scenarios, which does not match the reality. Second, it is believed that close friends in social space also have similar interests in interactive space and then indiscriminately adopt friends' opinions. To solve the above problems, this paper proposes a recommendation model based on generative adversarial network and social reconstruction (SRGAN). We propose a new adversarial framework to learn interactive data distribution. On the one hand, the generator selects friends who are similar to the user's personal preferences and considers the influence of friends on users from multiple angles to get their opinions. On the other hand, friends' opinions and users' personal preferences are distinguished by the discriminator. Then, the social reconstruction module is introduced to reconstruct the social network and constantly optimize the social relations of users, so that the social neighborhood can assist the recommendation effectively. Finally, the validity of our model is verified by experimental comparison with multiple social recommendation models on four datasets.

Continuous-flow self-supported seATRP using a sonicated microreactor.

Continuous-flow simplified electrochemically mediated atom transfer radical polymerization (seATRP) was achieved for the first time without supporting electrolytes (self-supported) using a novel sonicated tubular microreactor. Polymerizations of different acrylic monomers were carried out under different applied currents. The reaction was fast with 75% conversion achieved at ambient temperature in less than 27 minutes. Results also showed good evolution of molecular weight and maintained narrow molecular weight distribution. The reaction rate can be further manipulated by tuning the applied current. Sonication under proper conditions was found to be able to significantly improve both reaction rate and controllability. Self-supported reactions also enable more environmentally friendly and cost-effective operations.

Forecast of E-Commerce Transactions Trend Using Integration of Enhanced Whale Optimization Algorithm and Support Vector Machine.

E-commerce has become a crucial business model through the Internet around the world. Therefore, its transaction trend forecast can provide important information for the market planning and development in advance. For this purpose, the integrated model of enhanced whale optimization algorithm (EWOA) with support vector machine (SVM) is proposed for forecast of E-commerce transaction trend in this study. First, the global optimization ability of the whale optimization algorithm (WOA) is enhanced by the search updating strategy. Second, multiple factors that may affect the E-commerce transaction trend are analyzed and determined using the gray correlation mechanism. Third, the EWOA algorithm is employed to optimize the SVM random parameters. Finally, the EWOA-SVM model is established for forecasting E-commerce transaction trend. Two representative cases tests confirm that the EWOA-SVM model is superior to other existing methods in terms of fast convergence speed and high prediction accuracy.

Improved L0 Gradient Minimization with L1 Fidelity for Image Smoothing.

Edge-preserving image smoothing is one of the fundamental tasks in the field of computer graphics and computer vision. Recently, L0 gradient minimization (LGM) has been proposed for this purpose. In contrast to the total variation (TV) model which employs the L1 norm of the image gradient, the LGM model adopts the L0 norm and yields much better results for the piecewise constant image. However, as an improvement of the total variation (TV) model, the LGM model also suffers, even more seriously, from the staircasing effect and is not robust to noise. In order to overcome these drawbacks, in this paper, we propose an improvement of the LGM model by prefiltering the image gradient and employing the L1 fidelity. The proposed improved LGM (ILGM) behaves robustly to noise and overcomes the staircasing artifact effectively. Experimental results show that the ILGM is promising as compared with the existing methods.

Highly active and durable platinum-lead bimetallic alloy nanoflowers for formic acid electrooxidation.

The Pt84Pb16 (atomic ratio) bimetallic alloy nanoflowers (Pt84Pb16 BANFs) are synthesized by a simple one-pot hydrothermal reduction method that effectively enhance the dehydrogenation pathway of the formic acid oxidation reaction (FAOR) due to the ensemble effect and the electronic effect. As a result, the mass activity of Pt84Pb16 BANFs for the FAOR is 16.7 times higher than that of commercial Pt black at 0.3 V potential.