@inproceedings{0ca3ee0949674527942cdae4e3c1cdee,
title = "Deep news recommendation with contextual user profiling and multifaceted article representation",
abstract = "News recommendation is a new challenge in the current age of information overload. Making personalized recommendations from the sources of condense textual information is not trivial. It requires the understanding of both the news article{\textquoteright}s semantic meaning, and the user preferences via the user{\textquoteright}s history records. However, many existing methods are not capable to address the requirement. In this paper, we propose our novel news recommendation model called CUPMAR, that not only is able to learn the user-profile{\textquoteright}s preferences representation in multiple contexts, but also makes use of the multifaceted properties of news articles to provide personalized news recommendations. The main components of the CUPMAR model are the News Encoder (NE) and User-Profile Encoder (UE). The NE uses multiple properties of a news article with advanced neural network layers to derive news representation. The UE infers a user{\textquoteright}s long-term and recent preference contexts via her reading history to derive a user representation, and finds the most relevant candidate news for her. We evaluate our CUPMAR model with extensive experiments on the popular MIND dataset and demonstrate the strong performance of our approach. Our source code is also available online for the reproducibility purpose.",
keywords = "Attention mechanism, Contextual profile, Neural networks, News recommendation, Recommendation systems",
author = "Tran, {Dai Hoang} and Salma Hamad and Munazza Zaib and Abdulwahab Aljubairy and Sheng, {Quan Z.} and Zhang, {Wei Emma} and Tran, {Nguyen H.} and Khoa, {Nguyen Lu Dang}",
year = "2021",
doi = "10.1007/978-3-030-91560-5_17",
language = "English",
isbn = "9783030915599",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Springer Nature",
pages = "237--251",
editor = "Wenjie Zhang and Lei Zou and Zakaria Maamar and Lu Chen",
booktitle = "Web Information Systems Engineering – WISE 2021",
address = "United States",
note = "22nd International Conference on Web Information Systems Engineering, WISE 2021 ; Conference date: 26-10-2021 Through 29-10-2021",
}