Search Results for author: Ruining He

Found 12 papers, 5 papers with code

Deep Partial Multiplex Network Embedding

no code implementations5 Mar 2022 Qifan Wang, Yi Fang, Anirudh Ravula, Ruining He, Bin Shen, Jingang Wang, Xiaojun Quan, Dongfang Liu

Network embedding is an effective technique to learn the low-dimensional representations of nodes in networks.

Link Prediction Network Embedding +1

DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections

no code implementations EACL 2021 Yury Zemlyanskiy, Sudeep Gandhe, Ruining He, Bhargav Kanagal, Anirudh Ravula, Juraj Gottweis, Fei Sha, Ilya Eckstein

This enables a new class of powerful, high-capacity representations that can ultimately distill much of the useful information about an entity from multiple text sources, without any human supervision.

Knowledge Base Completion Natural Language Queries +3

Graph Convolutional Neural Networks for Web-Scale Recommender Systems

5 code implementations6 Jun 2018 Rex Ying, Ruining He, Kai-Feng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec

We develop a data-efficient Graph Convolutional Network (GCN) algorithm PinSage, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i. e., items) that incorporate both graph structure as well as node feature information.

Recommendation Systems

Translation-based Recommendation

1 code implementation8 Jul 2017 Ruining He, Wang-Cheng Kang, Julian McAuley

Modeling the complex interactions between users and items as well as amongst items themselves is at the core of designing successful recommender systems.

Recommendation Systems Translation

Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation

no code implementations28 Sep 2016 Ruining He, Julian McAuley

We show quantitatively that Fossil outperforms alternative algorithms, especially on sparse datasets, and qualitatively that it captures personalized dynamics and is able to make meaningful recommendations.

Sequential Recommendation

Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation

no code implementations15 Jul 2016 Ruining He, Chen Fang, Zhaowen Wang, Julian McAuley

Understanding users' interactions with highly subjective content---like artistic images---is challenging due to the complex semantics that guide our preferences.

Recommendation Systems

Sherlock: Sparse Hierarchical Embeddings for Visually-aware One-class Collaborative Filtering

no code implementations20 Apr 2016 Ruining He, Chunbin Lin, Jianguo Wang, Julian McAuley

Building successful recommender systems requires uncovering the underlying dimensions that describe the properties of items as well as users' preferences toward them.

Collaborative Filtering Recommendation Systems

Learning Compatibility Across Categories for Heterogeneous Item Recommendation

1 code implementation31 Mar 2016 Ruining He, Charles Packer, Julian McAuley

Identifying relationships between items is a key task of an online recommender system, in order to help users discover items that are functionally complementary or visually compatible.

Product Recommendation Recommendation Systems

Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering

no code implementations4 Feb 2016 Ruining He, Julian McAuley

Building a successful recommender system depends on understanding both the dimensions of people's preferences as well as their dynamics.

Collaborative Filtering Recommendation Systems

VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback

7 code implementations6 Oct 2015 Ruining He, Julian McAuley

In this paper we propose a scalable factorization model to incorporate visual signals into predictors of people's opinions, which we apply to a selection of large, real-world datasets.

Recommendation Systems

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