Knowledge-aware Complementary Product Representation Learning

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

Wrinkled few-layer graphene as highly efficient load bearer

Multilayered graphitic materials are not suitable as load-bearers due to their inherent weak interlayer bonding (for example, graphite is a solid lubricant in certain applications). This situation is largely improved when two-dimensional (2-D) materials such as a monolayer (SLG) graphene are employed. The downside in these cases is the presence of thermally or mechanically induced wrinkles which are ubiquitous in 2-D materials. Here we set out to examine the effect of extensive large wavelength/ amplitude wrinkling on the stress transfer capabilities of exfoliated simply-supported graphene flakes. Contrary to common belief we present clear evidence that this type of "corrugation" enhances the load bearing capacity of few-layer graphene as compared to 'flat' specimens. This effect is the result of the significant increase of the graphene/polymer interfacial shear stress per increment of applied strain due to wrinkling and paves the way for designing affordable graphene composites with highly improved stress-transfer efficiency.Comment: 20 pages, 6 figure

The Role of Preprocessing for Word Representation Learning in Affective Tasks

Affective tasks, including sentiment analysis, emotion classification, and sarcasm detection have drawn a lot of attention in recent years due to a broad range of useful applications in various domains. The main goal of affect detection tasks is to recognize states such as mood, sentiment, and emotions from textual data (e.g., news articles or product reviews). Despite the importance of utilizing preprocessing steps in different stages (i.e., word representation learning and building a classification model) of affect detection tasks, this topic has not been studied well. To that end, we explore whether applying various preprocessing methods (stemming, lemmatization, stopword removal, punctuation removal and so on) and their combinations in different stages of the affect detection pipeline can improve the model performance. The are many preprocessing approaches that can be utilized in affect detection tasks. However, their influence on the final performance depends on the type of preprocessing and the stages that they are applied. Moreover, the preprocessing impacts vary across different affective tasks. Our analysis provides thorough insights into how preprocessing steps can be applied in building an effect detection pipeline and their respective influence on performance

Conversation Derailment Forecasting with Graph Convolutional Networks

Online conversations are particularly susceptible to derailment, which can manifest itself in the form of toxic communication patterns like disrespectful comments or verbal abuse. Forecasting conversation derailment predicts signs of derailment in advance enabling proactive moderation of conversations. Current state-of-the-art approaches to address this problem rely on sequence models that treat dialogues as text streams. We propose a novel model based on a graph convolutional neural network that considers dialogue user dynamics and the influence of public perception on conversation utterances. Through empirical evaluation, we show that our model effectively captures conversation dynamics and outperforms the state-of-the-art models on the CGA and CMV benchmark datasets by 1.5\% and 1.7\%, respectively.Comment: WOAH, AC