Item trend learning for sequential recommendation system using gated graph neural network

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dc.contributor.authorTao, Y
dc.contributor.authorWang, C
dc.contributor.authorYao, L
dc.contributor.authorLi, W
dc.contributor.authorYu, Y
dc.date.accessioned2021-02-24T03:05:21Z
dc.date.available2021-02-24T03:05:21Z
dc.date.issued2021
dc.date.updated2021-02-24T01:34:03Z
dc.description.abstractRecommendation system, or recommender system, is widely used in online Web applications like e-commerce Web sites and movie review Web sites. Sequential recommender put more emphasis upon user’s short-term preference through exploiting information from its recent history. By incorporating the user short-term preference into the recommendation, the algorithm achieves a higher accuracy, which proves that a more accurate user portrait or representation boosts the performance to a great extent. Intuitionally, we seek to improve the current item representation modeling via incorporating the item trend information. Most of the recommendation models neglect the importance of the ever-changing item popularity. To this end, this paper introduces a novel sequential recommendation approach dubbed TRec. TRec learns the item trend information from the implicit user interaction history and incorporates the item trend information into the subsequent item recommendation tasks. After that, a self-attention mechanism is used for better representation. We also investigate alternative ways to model the proposed item trend representation; we evaluate two variant models which leverage the power of gated graph neural network upon the item trend representation modeling to boost the representation ability. We conduct extensive experiments with seven baseline methods on four benchmark datasets. The empirical results show that our proposed approach outperforms the state-of-the-art models as high as 18.2%. The experiment result displays the effectiveness in item trend information learning while with low computational complexity as well. Our study demonstrates the importance of item trend information in recommendation system.
dc.description.peerreviewedYes
dc.identifier.doi10.1007/s00521-021-05723-2
dc.identifier.issn0941-0643
dc.identifier.urihttp://hdl.handle.net/10072/402547
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.relation.ispartofjournalNeural Computing and Applications
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchCognitive and computational psychology
dc.subject.fieldofresearchcode4602
dc.subject.fieldofresearchcode5204
dc.titleItem trend learning for sequential recommendation system using gated graph neural network
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationTao, Y; Wang, C; Yao, L; Li, W; Yu, Y, Item trend learning for sequential recommendation system using gated graph neural network, Neural Computing and Applications, 2021
gro.description.notepublicThis publication has been entered as an advanced online version in Griffith Research Online.
gro.griffith.authorWang, Can
gro.hasfulltextNo Full Text
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