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dc.contributor.authorChen, Tong
dc.contributor.authorYin, Hongzhi
dc.contributor.authorNguyen, Quoc Viet Hung
dc.contributor.authorPeng, Wen-Chih
dc.contributor.authorLi, Xue
dc.contributor.authorZhou, Xiaofang
dc.date.accessioned2020-11-13T02:21:22Z
dc.date.available2020-11-13T02:21:22Z
dc.date.issued2020
dc.identifier.isbn9781728129037
dc.identifier.issn1084-4627
dc.identifier.doi10.1109/icde48307.2020.00125
dc.identifier.urihttp://hdl.handle.net/10072/399260
dc.description.abstractIn various web applications like targeted advertising and recommender systems, the available categorical features (e.g., product type) are often of great importance but sparse. As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for effective sparse predictive analytics. As the volume of web-scale data grows exponentially over time, sparse predictive analytics inevitably involves dynamic and sequential features. However, existing FM-based models assume no temporal orders in the data, and are unable to capture the sequential dependencies or patterns within the dynamic features, impeding the performance and adaptivity of these methods. Hence, in this paper, we propose a novel Sequence-Aware Factorization Machine (SeqFM) for temporal predictive analytics, which models feature interactions by fully investigating the effect of sequential dependencies. As static features (e.g., user gender) and dynamic features (e.g., user interacted items) express different semantics, we innovatively devise a multi-view self-attention scheme that separately models the effect of static features, dynamic features and the mutual interactions between static and dynamic features in three different views. In SeqFM, we further map the learned representations of feature interactions to the desired output with a shared residual network. To showcase the versatility and generalizability of SeqFM, we test SeqFM in three popular application scenarios for FM-based models, namely ranking, classification and regression tasks. Extensive experimental results on six large-scale datasets demonstrate the superior effectiveness and efficiency of SeqFM.
dc.description.peerreviewedYes
dc.publisherIEEE
dc.relation.ispartofconferencename2020 IEEE 36th International Conference on Data Engineering (ICDE 2020)
dc.relation.ispartofconferencetitle2020 IEEE 36th International Conference on Data Engineering (ICDE)
dc.relation.ispartofdatefrom2020-04-20
dc.relation.ispartofdateto2020-04-24
dc.relation.ispartoflocationDallas, USA
dc.relation.ispartofpagefrom1405
dc.relation.ispartofpageto1416
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchcode4602
dc.titleSequence-Aware Factorization Machines for Temporal Predictive Analytics
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationChen, T; Yin, H; Nguyen, QVH; Peng, W-C; Li, X; Zhou, X, Sequence-Aware Factorization Machines for Temporal Predictive Analytics, 2020 IEEE 36th International Conference on Data Engineering (ICDE), 2020, pp. 1405-1416
dc.date.updated2020-11-13T02:17:57Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorNguyen, Henry


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