Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

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/LRGCCF.Comment: The updated version is publised in AAAI 202

Biochemical properties of oxidases of Yali pear

The biochemical properties of polyphenol oxidase (PPO), peroxidase (POD) and ascorbate peroxidase (APX) from Yali pear were investigated. The optimum pH and temperature of three enzymes was 5.6, 4.0, 7.0 and 20, 40, 50°C, respectively. Enzyme kinetics results showed that the Michaelis constant (Km) and maximum velocity (Vmax) of PPO for catechol were 0.22 M and 1111 U/ml/min. The Km and Vmax values of POD for guaiacol were 0.14 M and 1429 U/ml/min. The Km of APX for ascorbic acid and H2O2 were 0.41 and 0.083 mM, respectively, and the Vmax of APX was 455 and 208 U/ml/min for ascorbic acid and H2O2, respectively. The inhibitory effects of the four inhibitors (ascorbic acid, citric acid, L-cysteine and phytic acid) on each enzyme were different, suggesting that the composite inhibitor is more appropriate for processing of Yali pear.Keywords: Yali pear, polyphenol oxidase, peroxidase, ascorbate peroxidase, biochemical propert

Lithium-Excess Research of Cathode Material Li2MnTiO4 for Lithium-Ion Batteries

Lithium-excess and nano-sized Li2+xMn1−x/2TiO4 (x = 0, 0.2, 0.4) cathode materials were synthesized via a sol-gel method. The X-ray diffraction (XRD) experiments indicate that the obtained main phases of Li2.0MnTiO4 and the lithium-excess materials are monoclinic and cubic, respectively. The scanning electron microscope (SEM) images show that the as-prepared particles are well distributed and the primary particles have an average size of about 20–30 nm. The further electrochemical tests reveal that the charge-discharge performance of the material improves remarkably with the lithium content increasing. Particularly, the first discharging capacity at the current of 30 mA g−1 increases from 112.2 mAh g−1 of Li2.0MnTiO4 to 187.5 mAh g−1 of Li2.4Mn0.8TiO4. In addition, the ex situ XRD experiments indicate that the monoclinic Li2MnTiO4 tends to transform to an amorphous state with the extraction of lithium ions, while the cubic Li2MnTiO4 phase shows better structural reversibility and stability

Generative Contrastive Graph Learning for Recommendation

By treating users' interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering(CF) based recommendation. Recently, researchers have introduced Graph Contrastive Learning(GCL) techniques into CF to alleviate the sparse supervision issue, which first constructs contrastive views by data augmentations and then provides self-supervised signals by maximizing the mutual information between contrastive views. Despite the effectiveness, we argue that current GCL-based recommendation models are still limited as current data augmentation techniques, either structure augmentation or feature augmentation. First, structure augmentation randomly dropout nodes or edges, which is easy to destroy the intrinsic nature of the user-item graph. Second, feature augmentation imposes the same scale noise augmentation on each node, which neglects the unique characteristics of nodes on the graph. To tackle the above limitations, we propose a novel Variational Graph Generative-Contrastive Learning(VGCL) framework for recommendation. Specifically, we leverage variational graph reconstruction to estimate a Gaussian distribution of each node, then generate multiple contrastive views through multiple samplings from the estimated distributions, which builds a bridge between generative and contrastive learning. Besides, the estimated variances are tailored to each node, which regulates the scale of contrastive loss for each node on optimization. Considering the similarity of the estimated distributions, we propose a cluster-aware twofold contrastive learning, a node-level to encourage consistency of a node's contrastive views and a cluster-level to encourage consistency of nodes in a cluster. Finally, extensive experimental results on three public datasets clearly demonstrate the effectiveness of the proposed model.Comment: This paper is accepted to SIGIR 2023. Code is avaliable: https://github.com/yimutianyang/SIGIR23-VGC

Assembling life history narratives from quantitative longitudinal panel data: what’s the story for families using social work?

Embedded within quantitative longitudinal panel or cohort studies is narrative potential that is arguably untapped but might enrich our understanding of individual and social lives across time. This paper discusses a methodology to assemble the life history narratives of families using social work by drawing on quantitative data from the British Household Panel Survey. It explores whether this person-centred approach helps us to understand the counterintuitive results of a parallel multivariate analyses, which suggest that families using social work fare worse than similar others over time. Our findings are tentative, due to the experimental use of this narrative method and the limits of social work information in the dataset. Nonetheless, the life histories presented bring to light complexities, diversity and the non-linear pathways between families’ needs, support and outcomes that the aggregates obscure. We conclude that reconstructing families’ lives in this way, especially in the absence of complementary longitudinal qualitative data, affords the wider opportunity to interrogate and better understand the findings of quantitative longitudinal studies

A review of independent living adaptations

This study will explore and test potential changes to the structural and delivery mechanisms for independent living adaptations. This is in order to move towards the Welsh Government goal of ensuring that adaptations can be delivered more speedily to those who need them. In doing so, it will help reduce inherent inequalities in the current system by ensuring joined up solutions, prevention and early intervention activity. This Review was commissioned in the light of some £35 million being spent annually by local authorities in Wales on Disabled Facilities Grants (DFGs) and the Welsh Government’s own spending of approximately £8 million on Physical Adaptations Grants (PAG) for social tenants. The Review was carried out between May and November 2014 and addressed three aims: mapping the current system performance Indicators testing the feasibility of options for change