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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better ... Wu, Chuhan; Wu, Fangzhao; Qi, Tao. - : arXiv, 2022 BASE Show details 2 Empowering News Recommendation with Pre-trained Language Models ... Wu, Chuhan; Wu, Fangzhao; Qi, Tao. - : arXiv, 2021 BASE Show details 3 Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Huang, Yongfeng; Qi, Tao. - : Underline Science Inc., 2021 BASE Show details 4 HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Huang, Yongfeng; Qi, Tao; Wu, Fangzhao; Wu, Chuhan; Xie, Xing; Yang, Peiru; Yu, t-yyu@microsoft.com. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.423 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 ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://underline.io/lecture/25849-hierec-hierarchical-user-interest-modeling-for-personalized-news-recommendation https://dx.doi.org/10.48448/jgzm-zj75 BASE Hide details 5 PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Huang, Yongfeng; Qi, Tao. - : Underline Science Inc., 2021 BASE Show details 6 Helpfulness-aware review based neural recommendation [<Journal>] Ge, Suyu [Verfasser]; Qi, Tao [Verfasser]; Wu, Chuhan [Verfasser]. DNB Subject Category Language Show details 7 An overview of studies on psychological well-being in children born following assisted reproductive technologies* Zhan, Qi-tao; Pan, Pei-pei; Xu, Xiang-rong. - : Zhejiang University Press, 2013 BASE Show details

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