249 papers with code • 1 benchmarks • 5 datasets
Feature engineering is the process of taking a dataset and constructing explanatory variables — features — that can be used to train a machine learning model for a prediction problem. Often, data is spread across multiple tables and must be gathered into a single table with rows containing the observations and features in the columns.
The traditional approach to feature engineering is to build features one at a time using domain knowledge, a tedious, time-consuming, and error-prone process known as manual feature engineering. The code for manual feature engineering is problem-dependent and must be re-written for each new dataset.
Second, for each pair of graphs, we train a cross-graph attention network which uses the node representations of an anchor graph to reconstruct another graph.
Ranked #2 on Graph Classification on D&D
Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance.
Ranked #18 on Named Entity Recognition on Ontonotes v5 (English)
Nonlinear classification models can predict future earnings surprises with a high accuracy by using pricing and earnings input data.
User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search.
In this paper, we study two instances of DeepFM where its "deep" component is DNN and PNN respectively, for which we denote as DeepFM-D and DeepFM-P. Comprehensive experiments are conducted to demonstrate the effectiveness of DeepFM-D and DeepFM-P over the existing models for CTR prediction, on both benchmark data and commercial data.
CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data.
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems.
Ranked #1 on Click-Through Rate Prediction on Company*
Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort.
Ranked #3 on Click-Through Rate Prediction on Bing News