Year: 2017
Type: conference-paper
Abstract: Many predictive tasks of web applications need to model categorical variables, such as user IDs and demographics like genders and occupations. To apply standard machine learning techniques, these categorical predictors are always converted to a set of binary features via one-hot encoding, making the resultant feature vector highly sparse. To learn from such sparse data effectively, it is crucial to account for the interactions between features.
Authors: Xiangnan He, Tat-Seng Chua
Institution: National University of Singapore
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Research Foundation
OpenAlex ID: W2963323306