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Found 12 papers, 5 papers with code
Online Matching: A Real-time Bandit System for Large-scale Recommendations
In this paper, we introduce Online Matching: a scalable closed-loop bandit system learning from users' direct feedback on items in real time.
Deep Partial Multiplex Network Embedding
Network embedding is an effective technique to learn the low-dimensional representations of nodes in networks.
DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections
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.
RealFormer: Transformer Likes Residual Attention
Transformer is the backbone of modern NLP models.
Ranked #4 on
Paraphrase Identification
on Quora Question Pairs
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
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.
Translation-based Recommendation
Modeling the complex interactions between users and items as well as amongst items themselves is at the core of designing successful recommender systems.
Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation
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.
Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation
Understanding users' interactions with highly subjective content---like artistic images---is challenging due to the complex semantics that guide our preferences.
Sherlock: Sparse Hierarchical Embeddings for Visually-aware One-class Collaborative Filtering
Building successful recommender systems requires uncovering the underlying dimensions that describe the properties of items as well as users' preferences toward them.
Learning Compatibility Across Categories for Heterogeneous Item Recommendation
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.
Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering
Building a successful recommender system depends on understanding both the dimensions of people's preferences as well as their dynamics.
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
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.
