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dc.contributor.authorLiu, Duen-Renen_US
dc.contributor.authorChen, Kuan-Yuen_US
dc.contributor.authorChou, Yun-Chengen_US
dc.contributor.authorLee, Jia-Hueien_US
dc.date.accessioned2019-04-02T06:04:46Z-
dc.date.available2019-04-02T06:04:46Z-
dc.date.issued2017-01-01en_US
dc.identifier.urihttp://dx.doi.org/10.1109/IIAI-AAI.2017.60en_US
dc.identifier.urihttp://hdl.handle.net/11536/150921-
dc.description.abstractThis research investigates an online recommendation method for new types of online news websites. Cross-domain analysis on user browsing news and the attending activities is conducted to predict user preferences on activities based on non-negative Matrix Factorization (NMF) and Latent Dirichlet Allocation (LDA) topic model. A novel approach is proposed for the dynamic adjustment of recommendation lists in order to tackle the issue of limited recommendation layouts. The existing studies have not addressed this issue. The proposed approach is implemented on an online news website and evaluated for online recommendations. The experiment results demonstrate that our method can predict user preferences on recommended activities and enhance the effectiveness of recommendations.en_US
dc.language.isoen_USen_US
dc.subjectRecommender systemen_US
dc.subjectOnline Recommendationen_US
dc.subjectData Miningen_US
dc.subjectLatent Topic Modelen_US
dc.subjectMatrix Factorizationen_US
dc.subjectDynamic Adjustment of Recommendation Listen_US
dc.titleAn Online Activity Recommendation Approach based on the Dynamic Adjustment of Recommendation Listsen_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1109/IIAI-AAI.2017.60en_US
dc.identifier.journal2017 6TH IIAI INTERNATIONAL CONGRESS ON ADVANCED APPLIED INFORMATICS (IIAI-AAI)en_US
dc.citation.spage407en_US
dc.citation.epage412en_US
dc.contributor.department資訊管理與財務金融系 註:原資管所+財金所zh_TW
dc.contributor.departmentDepartment of Information Management and Financeen_US
dc.identifier.wosnumberWOS:000454603400076en_US
dc.citation.woscount1en_US
顯示於類別:會議論文