@inproceedings{dong-etal-2022-text,
title = "Text-Aware Graph Embeddings for Donation Behavior Prediction",
author = "Dong, MeiXing and
Xu, Xueming and
Mihalcea, Rada",
editor = "Ustalov, Dmitry and
Gao, Yanjun and
Panchenko, Alexander and
Valentino, Marco and
Thayaparan, Mokanarangan and
Nguyen, Thien Huu and
Penn, Gerald and
Ramesh, Arti and
Jana, Abhik",
booktitle = "Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.textgraphs-1.7",
pages = "60--69",
abstract = "Predicting user behavior is essential for a large number of applications including recommender and dialog systems, and more broadly in domains such as healthcare, education, and economics. In this paper, we show that we can effectively predict donation behavior by using text-aware graph models, building upon graphs that connect user behaviors and their interests. Using a university donation dataset, we show that the graph representation significantly improves over learning from textual representations. Moreover, we show how incorporating implicit information inferred from text associated with the graph entities brings additional improvements. Our results demonstrate the role played by text-aware graph representations in predicting donation behavior.",
}
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<abstract>Predicting user behavior is essential for a large number of applications including recommender and dialog systems, and more broadly in domains such as healthcare, education, and economics. In this paper, we show that we can effectively predict donation behavior by using text-aware graph models, building upon graphs that connect user behaviors and their interests. Using a university donation dataset, we show that the graph representation significantly improves over learning from textual representations. Moreover, we show how incorporating implicit information inferred from text associated with the graph entities brings additional improvements. Our results demonstrate the role played by text-aware graph representations in predicting donation behavior.</abstract>
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%0 Conference Proceedings
%T Text-Aware Graph Embeddings for Donation Behavior Prediction
%A Dong, MeiXing
%A Xu, Xueming
%A Mihalcea, Rada
%Y Ustalov, Dmitry
%Y Gao, Yanjun
%Y Panchenko, Alexander
%Y Valentino, Marco
%Y Thayaparan, Mokanarangan
%Y Nguyen, Thien Huu
%Y Penn, Gerald
%Y Ramesh, Arti
%Y Jana, Abhik
%S Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F dong-etal-2022-text
%X Predicting user behavior is essential for a large number of applications including recommender and dialog systems, and more broadly in domains such as healthcare, education, and economics. In this paper, we show that we can effectively predict donation behavior by using text-aware graph models, building upon graphs that connect user behaviors and their interests. Using a university donation dataset, we show that the graph representation significantly improves over learning from textual representations. Moreover, we show how incorporating implicit information inferred from text associated with the graph entities brings additional improvements. Our results demonstrate the role played by text-aware graph representations in predicting donation behavior.
%U https://aclanthology.org/2022.textgraphs-1.7
%P 60-69
Markdown (Informal)
[Text-Aware Graph Embeddings for Donation Behavior Prediction](https://aclanthology.org/2022.textgraphs-1.7) (Dong et al., TextGraphs 2022)
ACL