Generalized Embedding Machines for Recommender Systems

02/16/2020
by   Enneng Yang, et al.
17

Factorization machine (FM) is an effective model for feature-based recommendation which utilizes inner product to capture second-order feature interactions. However, one of the major drawbacks of FM is that it couldn't capture complex high-order interaction signals. A common solution is to change the interaction function, such as stacking deep neural networks on the top of FM. In this work, we propose an alternative approach to model high-order interaction signals in the embedding level, namely Generalized Embedding Machine (GEM). The embedding used in GEM encodes not only the information from the feature itself but also the information from other correlated features. Under such situation, the embedding becomes high-order. Then we can incorporate GEM with FM and even its advanced variants to perform feature interactions. More specifically, in this paper we utilize graph convolution networks (GCN) to generate high-order embeddings. We integrate GEM with several FM-based models and conduct extensive experiments on two real-world datasets. The results demonstrate significant improvement of GEM over corresponding baselines.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/20/2020

AdnFM: An Attentive DenseNet based Factorization Machine for CTR Prediction

In this paper, we consider the Click-Through-Rate (CTR) prediction probl...
research
06/17/2022

Boosting Factorization Machines via Saliency-Guided Mixup

Factorization machines (FMs) are widely used in recommender systems due ...
research
07/26/2021

ContextNet: A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding

Click-through rate (CTR) estimation is a fundamental task in personalize...
research
08/16/2016

A Shallow High-Order Parametric Approach to Data Visualization and Compression

Explicit high-order feature interactions efficiently capture essential s...
research
06/20/2020

Enhancing Factorization Machines with Generalized Metric Learning

Factorization Machines (FMs) are effective in incorporating side informa...
research
09/16/2022

Serialized Interacting Mixed Membership Stochastic Block Model

Last years have seen a regain of interest for the use of stochastic bloc...
research
01/11/2021

Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction

Click-through rate (CTR) prediction, which aims to predict the probabili...

Please sign up or login with your details

Forgot password? Click here to reset