User Factor Adaptation for User Embedding via Multitask Learning

Published: Feb 22, 2021
Abstract
Language varies across users and their interested fields in social media data: words authored by a user across his/her interests may have different meanings (e.g., cool) or sentiments (e.g., fast). However, most of the existing methods to train user embeddings ignore the variations across user interests, such as product and movie categories (e.g., drama vs. action). In this study, we treat the user interest as domains and empirically examine how...
Paper Details
Title
User Factor Adaptation for User Embedding via Multitask Learning
Published Date
Feb 22, 2021
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