Recently, significant progress has been made in sequential recommendation with deep learning. Existing neural sequential recommendation models usually rely on the item prediction loss to learn model parameters or data representations. However, the model trained with this loss is prone to suffer from data sparsity problem. Since it overemphasizes the final performance, the association or fusion between context data and sequence data has not been well captured and utilized for sequential recommendation. To tackle this problem, we propose the model S 3-Rec, which stands for Self-Supervised learning for Sequential Recommendation, based on the self-attentive neural architecture. The main idea of our approach is to utilize the intrinsic data correlation to derive self-supervision signals and enhance the data representations via pre-training methods for improving sequential recommendation. For our task, we devise four auxiliary self-supervised objectives to learn the correlations among attribute, item, subsequence, and sequence by utilizing the mutual information maximization (MIM) principle. MIM provides a unified way to characterize the correlation between different types of data, which is particularly suitable in our scenario. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods, especially when only limited training data is available. Besides, we extend our self-supervised learning method to other recommendation models, which also improve their performance. CCS CONCEPTS • Information systems → Recommender systems. † Equal contribution.

Expression of receptor for hyaluronan-mediated motility (RHAMM), a breast cancer susceptibility gene, is tightly controlled in normal tissues but elevated in many tumors, contributing to tumorigenesis and metastases. However, how the expression of RHAMM is regulated remains elusive. Statins, inhibitors of mevalonate metabolic pathway widely used for hypercholesterolemia, have been found to also have antitumor effects, but little is known of the specific targets and mechanisms. Moreover, Hippo signaling pathway plays crucial roles in organ size control and cancer development, yet its downstream transcriptional targets remain obscure. Here we show that RHAMM expression is regulated by mevalonate and Hippo pathways converging onto Yes-associated protein (YAP)/TEAD, which binds RHAMM promoter at specific sites and controls its transcription and consequently breast cancer cell migration and invasion (BCCMI); and that simvastatin inhibits BCCMI via targeting YAP-mediated RHAMM transcription. Required for ERK phosphorylation and BCCMI, YAP-activated RHAMM transcription is dependent on mevalonate and sensitive to simvastatin, which modulate RHAMM transcription by modulating YAP phosphorylation and nuclear-cytoplasmic localization. Further, modulation by mevalonate/simvastatin of YAP-activated RHAMM transcription requires geranylgeranylation, Rho GTPase activation, and actin cytoskeleton rearrangement, but is largely independent of MST and LATS kinase activity. These findings from in vitro and in vivo investigations link mevalonate and Hippo pathways with RHAMM as a downstream effector, a YAP-transcription and simvastatin-inhibition target, and a cancer metastasis mediator; uncover a mechanism regulating RHAMM expression and cancer metastases; and reveal a mode whereby simvastatin exerts anticancer effects; providing potential targets for cancer therapeutic agents.

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Knowledge graphs capture structured information and relations between a set of entities or items. As such knowledge graphs represent an attractive source of information that could help improve recommender systems. However, existing approaches in this domain rely on manual feature engineering and do not allow for an end-to-end training. Here we propose Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS) to provide better recommendations. Conceptually, our approach computes user-specific item embeddings by first applying a trainable function that identifies important knowledge graph relationships for a given user. This way we transform the knowledge graph into a user-specific weighted graph and then apply a graph neural network to compute personalized item embeddings. To provide better inductive bias, we rely on label smoothness assumption, which posits that adjacent items in the knowledge graph are likely to have similar user relevance labels/scores. Label smoothness provides regularization over the edge weights and we prove that it is equivalent to a label propagation scheme on a graph. We also develop an efficient implementation that shows strong scalability with respect to the knowledge graph size. Experiments on four datasets show that our method outperforms state of the art baselines. KGNN-LS also achieves strong performance in cold-start scenarios where user-item interactions are sparse.

Much work has been devoted to supporting RDF data. But state-of-the-art systems and methods still cannot handle web scale RDF data effectively. Furthermore, many useful and general purpose graph-based operations (e.g., random walk, reachability, community discovery) on RDF data are not supported, as most existing systems store and index data in particular ways (e.g., as relational tables or as a bitmap matrix) to maximize one particular operation on RDF data: SPARQL query processing. In this paper, we introduce Trinity.RDF, a distributed, memory-based graph engine for web scale RDF data. Instead of managing the RDF data in triple stores or as bitmap matrices, we store RDF data in its native graph form. It achieves much better (sometimes orders of magnitude better) performance for SPARQL queries than the state-of-the-art approaches. Furthermore, since the data is stored in its native graph form, the system can support other operations (e.g., random walks, reachability) on RDF graphs as well. We conduct comprehensive experimental studies on real life, web scale RDF data to demonstrate the effectiveness of our approach.