@inproceedings{qi2021hierec,
author = {Qi, Tao and Wu, Fangzhao and Wu, Chuhan and Yang, Peiru and Yu, Yang and Xie, Xing and Huang, Yongfeng},
title = {HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation},
booktitle = {ACL-IJCNLP 2021},
year = {2021},
month = {June},
abstract = {User interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually learn a single user embedding for each user from their previous behaviors to represent their overall interest. However, user interest is usually diverse and multi-grained, which is difficult to be accurately modeled by a single user embedding. In this paper, we propose a news recommendation method with hierarchical user interest modeling, named HieRec. Instead of a single user embedding, in our method each user is represented in a hierarchical interest tree to better capture their diverse and multi-grained interest in news. We use a three-level hierarchy to represent 1) overall user interest; 2) user interest in coarse-grained topics like sports; and 3) user interest in fine-grained topics like football. Moreover, we propose a hierarchical user interest matching framework to match candidate news with different levels of user interest for more accurate user interest targeting. Extensive experiments on two real-world datasets validate our method can effectively improve the performance of user modeling for personalized news recommendation.},
url = {http://approjects.co.za/?big=en-us/research/publication/hierec-hierarchical-user-interest-modeling-for-personalized-news-recommendation/},
}