Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …

Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …

The MovieLens datasets are widely used in education, research, and industry. They are
downloaded hundreds of thousands of times each year, reflecting their use in popular press …

Graph convolutional network (GCN) has been successfully applied to many graph-based
applications; however, training a large-scale GCN remains challenging. Current SGD-based …

Graph neural networks have been extensively studied for learning with inter-connected data.
Despite this, recent evidence has revealed GNNs' deficiencies related to over-squashing …

A large amount of information exists in reviews written by users. This source of information
has been ignored by most of the current recommender systems while it can potentially …

Recently, many e-commerce websites have encouraged their users to rate shop** items
and write review texts. This review information has been very useful for understanding user …

We explore the properties of byte-level recurrent language models. When given sufficient
amounts of capacity, training data, and compute time, the representations learned by these …

Firms traditionally rely on interviews and focus groups to identify customer needs for
marketing strategy and product development. User-generated content (UGC) is a promising …

The domain of Aspect-based Sentiment Analysis, in which aspects are extracted, their
sentiments are analysed and sentiments are evolved over time, is getting much attention …