Neural Graph for Personalized Tag Recommendation

Yu, Yonghong, Chen, Xuewen, Zhang, Li, Gao, Rong and Gao, Haiyan (2020) Neural Graph for Personalized Tag Recommendation. IEEE Intelligent Systems. ISSN 1541-1672 (In Press)

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Official URL: https://doi.org/10.1109/MIS.2020.3040046

Abstract

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 firstly propose a graph neural networks boosted personalized tag recommendation model, namely NGTR, which integrates the graph neural networks into the pairwise interaction tensor factorization model. Specifically, we exploit the graph neural networks to capture the collaborative signal, and integrate the collaborative signal into the learning of representations of entities by transmitting and assembling the representations of neighbors along the interaction graphs. In addition, we also propose a light graph neural networks boosted personalized tag recommendation model, namely LNGTR. Different from NGTR, our proposed LNGTR model removes feature transformation and nonlinear activation components as well as adopts the weighted sum of the embeddings learned at all layers as the final embedding. Experimental results on real world datasets show that our proposed personalized tag recommendation models outperform the traditional tag recommendation methods.

Item Type: Article
Uncontrolled Keywords: Personalized Tag Recommendation Algorithm, Graph Neural Networks, Collaborative Signal, Collaboration, Tensors, Computational modeling, Intelligent systems, Training, Tagging, Semantics
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 16 Dec 2020 09:18
Last Modified: 31 Jul 2021 15:20
URI: http://nrl.northumbria.ac.uk/id/eprint/45014

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