Entity Embeddings

70 papers with code • 0 benchmarks • 2 datasets

Entity Embeddings is a technique for applying deep learning to tabular data. It involves representing the categorical data of an information systems entity with multiple dimensions.

Most implemented papers

Entity Embeddings of Categorical Variables

entron/entity-embedding-rossmann 22 Apr 2016

As entity embedding defines a distance measure for categorical variables it can be used for visualizing categorical data and for data clustering.

A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce

NeverInAsh/fit-recommendation 23 Jul 2019

To alleviate this problem, we propose a deep learning based content-collaborative methodology for personalized size and fit recommendation.

Scalable Zero-shot Entity Linking with Dense Entity Retrieval

facebookresearch/BLINK EMNLP 2020

This paper introduces a conceptually simple, scalable, and highly effective BERT-based entity linking model, along with an extensive evaluation of its accuracy-speed trade-off.

Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs

rstriv/Know-Evolve ICML 2017

The occurrence of a fact (edge) is modeled as a multivariate point process whose intensity function is modulated by the score for that fact computed based on the learned entity embeddings.

HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List Continuation

mvijaikumar/hypertenet 4 Oct 2021

The personalized list continuation (PLC) task is to curate the next items to user-generated lists (ordered sequence of items) in a personalized way.

Rethinking Graph Convolutional Networks in Knowledge Graph Completion

miralab-ustc/gcn4kgc 8 Feb 2022

Surprisingly, we observe from experiments that the graph structure modeling in GCNs does not have a significant impact on the performance of KGC models, which is in contrast to the common belief.

ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities

joker-xii/clusterea 20 May 2022

To tackle this challenge, we present ClusterEA, a general framework that is capable of scaling up EA models and enhancing their results by leveraging normalization methods on mini-batches with a high entity equivalent rate.

StarGraph: Knowledge Representation Learning based on Incomplete Two-hop Subgraph

hzli-ucas/stargraph 27 May 2022

Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector, ignoring the rich information contained in the neighborhood.

Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization

jyonn/cage 31 Aug 2023

Category information plays a crucial role in enhancing the quality and personalization of recommender systems.

Named Entity Disambiguation for Noisy Text

yotam-happy/NEDforNoisyText CONLL 2017

We address the task of Named Entity Disambiguation (NED) for noisy text.