完整後設資料紀錄
DC 欄位語言
dc.contributor.authorLiu, Duen-Renen_US
dc.contributor.authorChou, Yun-Chengen_US
dc.contributor.authorJian, Ciao-Tingen_US
dc.date.accessioned2020-02-02T23:54:28Z-
dc.date.available2020-02-02T23:54:28Z-
dc.date.issued1970-01-01en_US
dc.identifier.issn0368-492Xen_US
dc.identifier.urihttp://dx.doi.org/10.1108/K-08-2019-0578en_US
dc.identifier.urihttp://hdl.handle.net/11536/153499-
dc.description.abstractPurpose Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users' satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles. Design/methodology/approach Association rule mining is applied to users' news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website. Findings The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users' online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance. Originality/value Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user's online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.en_US
dc.language.isoen_USen_US
dc.subjectRecommender systemen_US
dc.subjectLatent Dirichlet allocationen_US
dc.subjectCollaborative topic modelingen_US
dc.subjectOnline recommendationsen_US
dc.subjectRecommendation diversityen_US
dc.titleIntegrating collaborative topic modeling and diversity for movie recommendations during news browsingen_US
dc.typeArticleen_US
dc.identifier.doi10.1108/K-08-2019-0578en_US
dc.identifier.journalKYBERNETESen_US
dc.citation.spage0en_US
dc.citation.epage0en_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:000506189800001en_US
dc.citation.woscount0en_US
顯示於類別:期刊論文