Precise user and item embedding learning is the key to building a successful recommender system. Traditionally, Collaborative Filtering (CF) provides a way to learn user and item embeddings from the user-item interaction history. However, the performance is limited due to the sparseness of user behavior data. With the emergence of online social networks, social recommender systems have been proposed to utilize each user's local neighbors' preferences to alleviate the data sparsity for better user embedding modeling. We argue that, for each user of a social platform, her potential embedding is influenced by her trusted users, with these trusted users are influenced by the trusted users' social connections. As social influence recursively propagates and diffuses in the social network, each user's interests change in the recursive process. Nevertheless, the current social recommendation models simply developed static models by leveraging the local neighbors of each user without simulating the recursive diffusion in the global social network, leading to suboptimal recommendation performance. In this paper, we propose a deep influence propagation model to stimulate how users are influenced by the recursive social diffusion process for social recommendation. For each user, the diffusion process starts with an initial embedding that fuses the related features and a free user latent vector that captures the latent behavior preference. The key idea of our proposed model is that we design a layer-wise influence propagation structure to model how users' latent embeddings evolve as the social diffusion process continues. We further show that our proposed model is general and could be applied when the user (item) attributes or the social network structure is not available. Finally, extensive experimental results on two real-world datasets clearly show the effectiveness of our proposed model 1 , with more than 13% performance improvements over the best baselines for top-10 recommendation on the two datasets.

Graph Convolutional Networks~(GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and non-linear activation operations. Recently, in Collaborative Filtering~(CF) based Recommender Systems~(RS), by treating the user-item interaction behavior as a bipartite graph, some researchers model higher-layer collaborative signals with GCNs. These GCN based recommender models show superior performance compared to traditional works. However, these models suffer from training difficulty with non-linear activations for large user-item graphs. Besides, most GCN based models could not model deeper layers due to the over smoothing effect with the graph convolution operation. In this paper, we revisit GCN based CF models from two aspects. First, we empirically show that removing non-linearities would enhance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second, we propose a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user-item interaction data. The proposed model is a linear model and it is easy to train, scale to large datasets, and yield better efficiency and effectiveness on two real datasets. We publish the source code at https://github.com/newlei/LR-GCCF.

Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences, which could alleviate the data sparsity issue in collaborative filtering based recommendation. Early approaches relied on utilizing each user's first-order social neighbors' interests for better user modeling, and failed to model the social influence diffusion process from the global social network structure. Recently, we propose a preliminary work of a neural influence Diff usion Network (i.e., DiffNet) for social recommendation [37]. DiffNet models the recursive social diffusion process for each user, such that the influence diffusion hidden in the higher-order social network is captured in the user embedding process. Despite the superior performance of DiffNet, we argue that, as users play a central role in both user-user social network and user-item interest network, only modeling the influence diffusion process in the social network would neglect the latent collaborative interests of users hidden in the user-item interest network. To this end, in this paper, we propose DiffNet++, an improved algorithm of DiffNet that models the neural influence diffusion and interest diffusion in a unified framework. By reformulating the social recommendation as a heterogeneous graph with social network and interest network as input, DiffNet++ advances DiffNet by injecting both the higher-order user latent interest reflected in the user-item graph and higher-order user influence reflected in the user-user graph for user embedding learning. This is achieved by iteratively aggregating each user's embedding from three aspects: the user's previous embedding, the influence aggregation of social neighbors from the social network, and the interest aggregation of item neighbors from the user-item interest network. Furthermore, we design a multi-level attention network that learns how to attentively aggregate user embeddings from these three aspects. Finally, extensive experimental results on two real-world datasets clearly show the effectiveness of our proposed model.

The production and detoxification of reactive oxygen intermediates (ROIs) play an important role in the plant response to nutrient and environmental stresses. The present study demonstrated the behavior of growth, ROIs-production and their detoxification in primed and non-primed rice seedlings under chilling stress (18°C) and nitrogen-(N), phosphorus-(P), or potassium-(K) deprivation. The results revealed that chilling stress as well as deprivation of any mineral nutrient severely hampered the seedling growth of rice, however, seed priming treatments (particularly selenium- or salicylic acid-priming), were effective in enhancing the rice growth under stress conditions. The N-deprivation caused the maximum reduction in shoot growth, while the root growth was only decreased by P- or K-deprivation. Although, N-deprivation enhanced the root length of rice, the root fresh weight was unaffected. Rate of lipid peroxidation as well as the production of ROIs, was generally increased under stress conditions; the K-deprived seedlings recorded significantly lower production of ROIs than N- or P-deprived seedlings. The responses of enzymatic and non-enzymatic antioxidants in rice seedlings to chilling stress were variable with nutrient management regime. All the seed priming were found to trigger or at least maintain the antioxidant defense system of rice seedlings. Notably, the levels of ROIs were significantly reduced by seed priming treatments, which were concomitant with the activities of ROIs-producing enzymes (monoamine oxidase and xanthine oxidase), under all studied conditions. Based on these findings, we put forward the hypothesis that along with role of ROIs-scavenging enzymes, the greater tolerance of primed rice seedlings can also be due to the reduced activity of ROIs-producing enzymes.

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