Most existing collaborative filtering techniques have focused on modeling the binary relation
of users to items by extracting from user ratings. Aside from users' ratings, their affiliated …

The deluge of digital information in our daily life—from user-generated content, such as
microblogs and scientific papers, to online business, such as viral marketing and advertising …

We propose Factual News Graph (FANG), a novel graphical social context representation
and learning framework for fake news detection. Unlike previous contextual models that …

Many predictive tasks of web applications need to model categorical variables, such as user
IDs and demographics like genders and occupations. To apply standard machine learning …

In recent years, deep neural networks have yielded immense success on speech
recognition, computer vision and natural language processing. However, the exploration of …

This paper contributes improvements on both the effectiveness and efficiency of Matrix
Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing …

Collaborative Filtering (CF)-based recommendation algorithms, such as Latent Factor
Models (LFM), work well in terms of prediction accuracy. However, the latent features make it …

The bipartite graph is a ubiquitous data structure that can model the relationship between
two entity types: for instance, users and items, queries and webpages. In this paper, we …

Micro-videos, a new form of user generated contents (UGCs), are gaining increasing
enthusiasm. Popular micro-videos have enormous commercial potential in many ways, such …

Prediction of popularity has profound impact for social media, since it offers opportunities to
reveal individual preference and public attention from evolutionary social systems. Previous …