Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm. / Chen, Xuewen ; Yu, Yonghong; Jiang, Fengyixin ; Zhang, Li; Gao, Rong; Gao, Haiyan .

Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm. Glasgow : IEEE, 2020.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published

Standard

Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm. / Chen, Xuewen ; Yu, Yonghong; Jiang, Fengyixin ; Zhang, Li; Gao, Rong; Gao, Haiyan .

Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm. Glasgow : IEEE, 2020.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Chen, X, Yu, Y, Jiang, F, Zhang, L, Gao, R & Gao, H 2020, Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm. in Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm. IEEE, Glasgow, International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/IJCNN48605.2020.9207610

APA

Chen, X., Yu, Y., Jiang, F., Zhang, L., Gao, R., & Gao, H. (2020). Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm. In Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm IEEE. https://doi.org/10.1109/IJCNN48605.2020.9207610

Vancouver

Chen X, Yu Y, Jiang F, Zhang L, Gao R, Gao H. Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm. In Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm. Glasgow: IEEE. 2020 https://doi.org/10.1109/IJCNN48605.2020.9207610

Author

Chen, Xuewen ; Yu, Yonghong ; Jiang, Fengyixin ; Zhang, Li ; Gao, Rong ; Gao, Haiyan . / Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm. Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm. Glasgow : IEEE, 2020.

BibTeX

@inproceedings{3fd55da013d24de5b872d07b50b28704,
title = "Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm",
abstract = "Personalized tag recommender systems recommend a set of tags for items based on users' historical behaviors, and play an important role in the collaborative tagging systems. However, traditional personalized tag recommendation methods cannot guarantee that the collaborative signal hidden in the interactions among entities is effectively encoded in the process of learning the representations of entities, resulting in insufficient expressive capacity for characterizing the preferences or attributes of entities. In this paper, we proposed a graph neural networks boosted personalized tag recommendation model, which integrates the graph neural networks into the pairwise interaction tensor factorization model. Specifically, we consider two types of interaction graph (i.e. the user-tag interaction graph and the item-tag interaction graph) that is derived from the tag assignments. For each interaction graph, we exploit the graph neural networks to capture the collaborative signal that is encoded in the interaction graph and integrate the collaborative signal into the learning of representations of entities by transmitting and assembling the representations of entity neighbors along the interaction graphs. In this way, we explicitly capture the collaborative signal, resulting in rich and meaningful representations of entities. Experimental results on real world datasets show that our proposed graph neural networks boosted personalized tag recommendation model outperforms the traditional tag recommendation models.",
author = "Xuewen Chen and Yonghong Yu and Fengyixin Jiang and Li Zhang and Rong Gao and Haiyan Gao",
year = "2020",
month = sep,
day = "28",
doi = "10.1109/IJCNN48605.2020.9207610",
language = "English",
isbn = "9781728169279",
booktitle = "Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm",
publisher = "IEEE",
note = "International Joint Conference on Neural Networks (IJCNN) ; Conference date: 19-07-2020 Through 24-07-2020",

}

RIS

TY - GEN

T1 - Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm

AU - Chen, Xuewen

AU - Yu, Yonghong

AU - Jiang, Fengyixin

AU - Zhang, Li

AU - Gao, Rong

AU - Gao, Haiyan

PY - 2020/9/28

Y1 - 2020/9/28

N2 - Personalized tag recommender systems recommend a set of tags for items based on users' historical behaviors, and play an important role in the collaborative tagging systems. However, traditional personalized tag recommendation methods cannot guarantee that the collaborative signal hidden in the interactions among entities is effectively encoded in the process of learning the representations of entities, resulting in insufficient expressive capacity for characterizing the preferences or attributes of entities. In this paper, we proposed a graph neural networks boosted personalized tag recommendation model, which integrates the graph neural networks into the pairwise interaction tensor factorization model. Specifically, we consider two types of interaction graph (i.e. the user-tag interaction graph and the item-tag interaction graph) that is derived from the tag assignments. For each interaction graph, we exploit the graph neural networks to capture the collaborative signal that is encoded in the interaction graph and integrate the collaborative signal into the learning of representations of entities by transmitting and assembling the representations of entity neighbors along the interaction graphs. In this way, we explicitly capture the collaborative signal, resulting in rich and meaningful representations of entities. Experimental results on real world datasets show that our proposed graph neural networks boosted personalized tag recommendation model outperforms the traditional tag recommendation models.

AB - Personalized tag recommender systems recommend a set of tags for items based on users' historical behaviors, and play an important role in the collaborative tagging systems. However, traditional personalized tag recommendation methods cannot guarantee that the collaborative signal hidden in the interactions among entities is effectively encoded in the process of learning the representations of entities, resulting in insufficient expressive capacity for characterizing the preferences or attributes of entities. In this paper, we proposed a graph neural networks boosted personalized tag recommendation model, which integrates the graph neural networks into the pairwise interaction tensor factorization model. Specifically, we consider two types of interaction graph (i.e. the user-tag interaction graph and the item-tag interaction graph) that is derived from the tag assignments. For each interaction graph, we exploit the graph neural networks to capture the collaborative signal that is encoded in the interaction graph and integrate the collaborative signal into the learning of representations of entities by transmitting and assembling the representations of entity neighbors along the interaction graphs. In this way, we explicitly capture the collaborative signal, resulting in rich and meaningful representations of entities. Experimental results on real world datasets show that our proposed graph neural networks boosted personalized tag recommendation model outperforms the traditional tag recommendation models.

U2 - 10.1109/IJCNN48605.2020.9207610

DO - 10.1109/IJCNN48605.2020.9207610

M3 - Conference contribution

SN - 9781728169279

BT - Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm

PB - IEEE

CY - Glasgow

T2 - International Joint Conference on Neural Networks (IJCNN)

Y2 - 19 July 2020 through 24 July 2020

ER -