Paper Info
Title
METoNR: A meta explanation triplet oriented news recommendation model
Abstract
Personalized news recommendation is an important task for online news platforms to target user interests and alleviate information overload. Most existing methods leverage news contents to make recommendation due to the importance of contents to distinguish pieces of news. Although Heterogeneous Graph (HG) shows great potential in organizing and exploiting varieties of side information to boost recommendation performance in general recommender systems, and at the same time there exist rich side information in real news recommendation scenario, existing methods lack attention to utilizing HG to exploit multiple kinds of side information to enhance news recommendation accuracy. In addition, they pay no attention to providing understandable explanations to improve user satisfaction. To this end, we propose a meta explanation triplet oriented news recommendation model. Specifically, we first construct an HG to extract high-order relatedness knowledge between users and news from various side information. Then, a dedicated neural network is designed to leverage rich side information and contents in a joint way to make news recommendation. Finally, we provide user-centered and news-centered recommendation explanations for users based on meta explanation triplets. Extensive experiments on two benchmark real-world datasets show that our model could improve news recommendation performance compared with state-of-the-art methods and provide effective explanations.
Year
DOI
Venue
2022
10.1016/j.knosys.2021.107922
Knowledge-Based Systems
Keywords
DocType
Volume
Knowledge extraction,News recommendation,Meta explanation triplet,Supervised multi-task learning,Recommendation explanation
Journal
238
ISSN
Citations 
PageRank 
0950-7051
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Mingwei Zhang1102.52
Guiping Wang200.34
Lanlan Ren300.34
Jianxin Li444348.67
Ke Deng555041.41
Bin Zhang6228.26