Learning product representations that reflect complementary relationship plays a central role in e-commerce recommender system. In the absence of the product relationships graph, which existing methods rely on, there is a need to detect the complementary relationships directly from noisy and sparse customer purchase activities. Furthermore, unlike simple relationships such as similarity, complementariness is asymmetric and non-transitive. Standard usage of representation learning emphasizes on only one set of embedding, which is problematic for modelling such properties of complementariness.We propose using knowledge-aware learning with dual product embedding to solve the above challenges. We encode contextual knowledge into product representation by multi-task learning, to alleviate the sparsity issue. By explicitly modelling with user bias terms, we separate the noise of customer-specific preferences from the complementariness. Furthermore, we adopt the dual embedding framework to capture the intrinsic properties of complementariness and provide geometric interpretation motivated by the classic separating hyperplane theory. Finally, we propose a Bayesian network structure that unifies all the components, which also concludes several popular models as special cases.The proposed method compares favourably to state-of-art methods, in downstream classification and recommendation tasks. We also develop an implementation that scales efficiently to a dataset with millions of items and customers.

Large-scale neuroimaging studies have been collecting brain images of study individuals, which take the form of two-dimensional, three-dimensional, or higher dimensional arrays, also known as tensors. Addressing scientific questions arising from such data demands new regression models that take multidimensional arrays as covariates. Simply turning an image array into a long vector causes extremely high dimensionality that compromises classical regression methods, and, more seriously, destroys the inherent spatial structure of array data that possesses wealth of information. In this article, we propose a family of generalized linear tensor regression models based upon the Tucker decomposition of regression coefficient arrays. Effectively exploiting the low rank structure of tensor covariates brings the ultrahigh dimensionality to a manageable level that leads to efficient estimation. We demonstrate, both numerically that the new model could provide a sound recovery of even high rank signals, and asymptotically that the model is consistently estimating the best Tucker structure approximation to the full array model in the sense of Kullback-Liebler distance. The new model is also compared to a recently proposed tensor regression model that relies upon an alternative CANDECOMP/PARAFAC (CP) decomposition.

A chiral Brønsted base catalyzed asymmetric annulation of ortho‐alkynylanilines has been developed to access axially chiral naphthyl‐C2‐indoles via vinylidene ortho‐quinone methide (VQM) intermediates. This strategy provides a unique organocatalytic atroposelective route to axially chiral aryl‐C2‐indole skeletons with excellent enantioselectivity and functional‐group tolerance. This transformation was applicable to decagram‐scale preparation (50.0 g) with perfect enantioselectivity through simple recrystallization. Moreover, the utility of this reaction was demonstrated by a variety of transformations towards chiral naphthyl‐C2‐indoles for a series of carbon–heteroatom bond formations. Furthermore, the prepared axially chiral naphthyl‐C2‐indoles were applied as a chiral skeleton for organocatalytic aza‐Baylis–Hillman reaction and asymmetric formal [4+2] tandem cyclization to give the corresponding adducts in high yields with improved enantioselectivity and diastereoselectivity.

In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce. We define the key entities and summarize the pivotal product relations that are critical for general e-commerce applications including marketing, advertisement, search ranking and recommendation. We first provide a comprehensive comparison between PKG and ordinary knowledge graph (KG) and then illustrate why KG embedding methods are not suitable for PKG learning. We construct a self-attention-enhanced distributed representation learning model for learning PKG embeddings from raw customer activity data in an end-to-end fashion. We design an effective multi-task learning schema to fully leverage the multi-modal e-commerce data. The Poincaré embedding is also employed to handle complex entity structures. We use a real-world dataset from grocery.walmart.com to evaluate the performances on knowledge completion, search ranking and recommendation. The proposed approach compares favourably to baselines in knowledge completion and downstream tasks. CCS CONCEPTS• Information systems → Data mining; Web searching and information discovery; Retrieval models and ranking.

High‐temperature polymer dielectrics are in great demand for harsh‐environment applications. Maintaining high‐energy storage density and low loss at elevated temperatures remains a major challenge for polymer dielectrics. In this work, a new type of polymer dielectric material is designed, which exhibits comparable dielectric properties in the start‐of‐the‐art dielectric nanocomposites and a superior potential for scale up. A soluble, glassy state polymer with a polarizing group is designed by introducing a weakly polar group into the polyaramid (PA) backbone to dilute the hydrogen bonding of the PA parent species. This increases the mobility of the molecular dipole within the polymer in the glassy state, thereby increasing its dielectric constant while maintaining the high‐temperature performance. The result of this design is a polymer with a glass transition temperature of 251 °C, a dielectric constant of up to 4.5, and a dielectric loss of 1%, while maintaining 2.1 J cm−3 energy density and 86.8% efficiency at 200 °C. This polymer, with its excellent, intrinsic, electrical‐energy‐storage properties can also be adapted for a roll‐to‐roll capacitor film production. Breaking intermolecular hydrogen bonds to enhance the electrical‐energy‐storage properties of polymers is an excellent path for designing polymer dielectrics with high‐temperature capabilities.