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dc.contributor.authorLiu, Chien-Liangen_US
dc.contributor.authorWu, Xuan-Weien_US
dc.date.accessioned2017-04-21T06:55:17Z-
dc.date.available2017-04-21T06:55:17Z-
dc.date.issued2016-12-01en_US
dc.identifier.issn0957-4174en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2016.08.009en_US
dc.identifier.urihttp://hdl.handle.net/11536/134189-
dc.description.abstractThis work devises a factorization model called compact latent factor model, in which we propose a compact representation to consider query, user and item in the model. The blend of information retrieval and collaborative filtering is a typical setting in many applications. The proposed model can incorporate various features into the model, and this work demonstrates that the proposed model can incorporate context-aware and content-based features to handle context-aware recommendation and cold-start problems, respectively. Besides recommendation accuracy, a critical problem concerning the computational cost emerges in practical situations. To tackle this problem, this work uses a buffer update scheme to allow the proposed model to process data incrementally, and provide a means to use historical data instances. Meanwhile, we use stochastic gradient descent algorithm along with sampling technique to optimize ranking loss, giving a competitive performance while considering scalability and deployment issues. The experimental results indicate that the proposed algorithm outperforms other alternatives on four datasets. (C) 2016 Elsevier Ltd. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectRecommender systemen_US
dc.subjectLatent factor modelen_US
dc.subjectCollaborative filteringen_US
dc.subjectContent-baseden_US
dc.titleLarge-scale recommender system with compact latent factor modelen_US
dc.identifier.doi10.1016/j.eswa.2016.08.009en_US
dc.identifier.journalEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.citation.volume64en_US
dc.citation.spage467en_US
dc.citation.epage475en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000383810800037en_US
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