ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation

Recommender system (RS) devotes to predicting user preference to a given item and has been widely deployed in most web-scale applications. Recently, knowledge graph (KG) attracts much attention in RS due to its abundant connective information. Existing methods either explore independent meta-paths for user-item pairs over KG, or employ graph neural network (GNN) on whole KG to produce representations for users and items separately. Despite effectiveness, the former type of methods fails to fully capture structural information implied in KG, while the latter ignores the mutual effect between target user and item during the embedding propagation. In this work, we propose a new framework named Adaptive Target-Behavior Relational Graph network (ATBRG for short) to effectively capture structural relations of target user-item pairs over KG. Specifically, to associate the given target item with user behaviors over KG, we propose the graph connect and graph prune techniques to construct adaptive target-behavior relational graph. To fully distill structural information from the sub-graph connected by rich relations in an end-to-end fashion, we elaborate on the model design of ATBRG, equipped with relation-aware extractor layer and representation activation layer. We perform extensive experiments on both industrial and benchmark datasets. Empirical results show that ATBRG consistently and significantly outperforms state-of-the-art methods. Moreover, ATBRG has also achieved a performance improvement of 5.1% on CTR metric after successful deployment in one popular recommendation scenario of Taobao APP.Comment: Accepted by SIGIR 2020, full paper with 10 pages and 5 figure

MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction

Click-through rate (CTR) prediction is a critical task for many industrial systems, such as display advertising and recommender systems. Recently, modeling user behavior sequences attracts much attention and shows great improvements in the CTR field. Existing works mainly exploit attention mechanism based on embedding product when considering relations between user behaviors and target item. However, this methodology lacks of concrete semantics and overlooks the underlying reasons driving a user to click on a target item. In this paper, we propose a new framework named Multiplex Target-Behavior Relation enhanced Network (MTBRN) to leverage multiplex relations between user behaviors and target item to enhance CTR prediction. Multiplex relations consist of meaningful semantics, which can bring a better understanding on users' interests from different perspectives. To explore and model multiplex relations, we propose to incorporate various graphs (e.g., knowledge graph and item-item similarity graph) to construct multiple relational paths between user behaviors and target item. Then Bi-LSTM is applied to encode each path in the path extractor layer. A path fusion network and a path activation network are devised to adaptively aggregate and finally learn the representation of all paths for CTR prediction. Extensive offline and online experiments clearly verify the effectiveness of our framework.Comment: Accepted by CIKM202

Downregulation of hypoxia-inducible factor-1α by RNA interference alleviates the development of collagen-induced arthritis in rats

Rheumatoid arthritis (RA) is the most common type of autoimmune arthritis. Hypoxia-inducible factor-1α (HIF-1α) as a transcription factor in response to hypoxia suggests that it could be a potential therapeutic target for the treatment of RA. In this study, we assessed whether the HIF pathway blockade attenuates the manifestations of RA in the collagen-induced arthritis (CIA) rat model. We constructed a short hairpin RNA (shRNA) lentiviral expression vector targeting HIF-1α (pLVX-shRNA-HIF-1α) and to achieve HIF-1α RNA interference. Quantitative RT-PCR, immunofluorescence staining, and western blot were used to detect the expressions of HIF-1α, vascular endothelial growth factor (VEGF), phsopho (p)-p65, and p-IКBɑ mRNA and protein, respectively. Micro-computed tomography was used to investigate joint morphology at different time points after CIA induction. Moreover, enzyme-linked immunosorbent assay (ELISA) was used to monitor the expression of inflammatory cytokines. In vitro analyses revealed that pLVX-shRNA-HIF-1α effectively inhibited the expression of HIF-1α and VEGF and led to the activation of p-65 and p-IКBɑ, as well as decreased proinflammatory cytokine expression in cell culture. Inhibition of HIF-1α in rats decreased signs of a systemic inflammatory condition, together with decreased pathological changes of RA. Moreover, downregulation of HIF-1α expression markedly reduced the synovitis and angiogenesis. In conclusion, we have shown that pharmacological inhibition of HIF-1 may improve the clinical manifestations of RA

Effect of Topological Defects on Buckling Behavior of Single-walled Carbon Nanotube

Molecular dynamic simulation method has been employed to consider the critical buckling force, pressure, and strain of pristine and defected single-walled carbon nanotube (SWCNT) under axial compression. Effects of length, radius, chirality, Stone–Wales (SW) defect, and single vacancy (SV) defect on buckling behavior of SWCNTs have been studied. Obtained results indicate that axial stability of SWCNT reduces significantly due to topological defects. Critical buckling strain is more susceptible to defects than critical buckling force. Both SW and SV defects decrease the buckling mode of SWCNT. Comparative approach of this study leads to more reliable design of nanostructures