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Towards High-Order Complementary Recommendation via Logical Reasoning Network

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dc.contributor.authorWu, Longfengen
dc.contributor.authorZhou, Yaoen
dc.contributor.authorZhou, Daweien
dc.date.accessioned2023-03-01T13:49:47Zen
dc.date.available2023-03-01T13:49:47Zen
dc.date.issued2022-11en
dc.date.updated2023-03-01T04:14:38Zen
dc.description.abstractComplementary recommendation gains increasing attention in e-commerce since it expedites the process of finding frequently-bought-with products for users in their shopping journey. Therefore, learning the product representation that can reflect this complementary relationship plays a central role in modern recommender systems. In this work, we propose a logical reasoning network, LOGIREC, to effectively learn embeddings of products as well as various transformations (projection, intersection, negation) between them. LOGIREC is capable of capturing the asymmetric complementary relationship between products and seamlessly extending to high-order recommendations where more comprehensive and meaningful complementary relationship is learned for a query set of products. Finally, we further propose a hybrid network that is jointly optimized for learning a more generic product representation. We demonstrate the effectiveness of our LOGIREC on multiple public real-world datasets in terms of various ranking-based metrics under both low-order and high-order recommendation scenarios.en
dc.description.versionAccepted versionen
dc.format.extentPages 1227-1232en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/ICDM54844.2022.00159en
dc.identifier.isbn9781665450997en
dc.identifier.issn1550-4786en
dc.identifier.urihttp://hdl.handle.net/10919/114015en
dc.identifier.volume2022-Novemberen
dc.language.isoenen
dc.publisherIEEEen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleTowards High-Order Complementary Recommendation via Logical Reasoning Networken
dc.title.serialProceedings - IEEE International Conference on Data Mining, ICDMen
dc.typeConference proceedingen
dc.type.dcmitypeTexten
dc.type.otherConference Proceedingen
pubs.finish-date2022-12-01en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Computer Scienceen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.start-date2022-11-28en

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