Hanwang Zhang

Recommendation Technologies for Multimedia Content

2018

Recommendation systems play a vital role in online information systems and have become a major monetization tool for user-oriented platforms. In recent years, there has been increasing research interest in recommendation technologies in the information …

KQA Pro: A Large-Scale Dataset with Interpretable Programs and Accurate SPARQLs for Complex Question Answering over Knowledge Base

2020 · arXiv (Cornell University)

Complex question answering over knowledge base (Complex KBQA) is challenging because it requires various compositional reasoning capabilities, such as multi-hop inference, attribute comparison, set operation, and etc. Existing benchmarks have some shortcomings that limit the …

Cross-Domain Empirical Risk Minimization for Unbiased Long-Tailed Classification

2022 · Proceedings of the AAAI Conference on Artificial Intelligence

We address the overlooked unbiasedness in existing long-tailed classification methods: we find that their overall improvement is mostly attributed to the biased preference of "tail" over "head", as the test distribution is assumed to be …

Reasoning Physical Video Generation with Diffusion Timestep Tokens via Reinforcement Learning

2025 · arXiv (Cornell University)

Despite recent progress in video generation, producing videos that adhere to physical laws remains a significant challenge. Traditional diffusion-based methods struggle to extrapolate to unseen physical conditions (eg, velocity) due to their reliance on data-driven …

Learning Image and User Features for Recommendation in Social Networks

2015

Good representations of data do help in many machine learning tasks such as recommendation. It is often a great challenge for traditional recommender systems to learn representative features of both users and images in large …

Discrete Collaborative Filtering

2016

We address the efficiency problem of Collaborative Filtering (CF) by hashing users and items as latent vectors in the form of binary codes, so that user-item affinity can be efficiently calculated in a Hamming space. …

Fast Matrix Factorization for Online Recommendation with Implicit Feedback

2016

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 works. First, due to the large space of unobserved feedback, …

Neural Collaborative Filtering

2017 · arXiv (Cornell University)

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In …

Attentive Collaborative Filtering

2017

Multimedia content is dominating today's Web information. The nature of multimedia user-item interactions is 1/0 binary implicit feedback (e.g., photo likes, video views, song downloads, etc.), which can be collected at a larger scale with …

Attributed Social Network Embedding

2018 · IEEE Transactions on Knowledge and Data Engineering

Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. However, most existing methods focused only on leveraging network structure. For social …

Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

2017

Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions. Despite effectiveness, FM can be hindered by its modelling of all feature interactions with the …