Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm

Xuewen Chen, Yonghong Yu, Fengyixin Jiang , Li Zhang, Rong Gao, Haiyan Gao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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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.
Original languageEnglish
Title of host publication2020 IEEE World Congress on Computational Intelligence (WCCI) - International Joint Conference on Neural Networks (IJCNN)
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages8
ISBN (Electronic)9781728169262
ISBN (Print)9781728169279
DOIs
Publication statusPublished - 19 Jul 2020
EventIJCNN 2020: The International Joint Conference on Neural Networks - Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020
https://wcci2020.org/

Conference

ConferenceIJCNN 2020
CountryUnited Kingdom
CityGlasgow
Period19/07/2024/07/20
Internet address

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