完整後設資料紀錄
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dc.contributor.authorLiu, Chien-Liangen_US
dc.contributor.authorChen, Ying-Chuanen_US
dc.date.accessioned2019-04-02T06:00:43Z-
dc.date.available2019-04-02T06:00:43Z-
dc.date.issued2018-11-01en_US
dc.identifier.issn0950-7051en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.knosys.2018.07.001en_US
dc.identifier.urihttp://hdl.handle.net/11536/148236-
dc.description.abstractMany mobile devices are equipped with video shooting function, and users tend to use these mobile devices to produce user generated content (UGC), and share with friends or the public owing to the popularity of social media. To make the video to be attractive, embedding appropriate background music into the video is a popular way to enrich user experience, but it is a time-consuming and labor-intensive task to find music that fits the video. This work proposes to use latent factors to recommend a list of music songs for a given video, in which the recommendation is based on the proposed score function, which involves the weighted average of the latent factors for the video and music. Moreover, we use pairwise ranking to design the objective function, and use stochastic gradient descent to optimize the proposed objective function. In the experiments, we specify two hypotheses and design several experiments to assess the performance and the effectiveness of the proposed algorithm from different aspects, including accuracy, quantitative research, and qualitative research. The experimental results indicate that the proposed model is promising in accuracy and quantitative research. Furthermore, this work provides detailed analysis to investigate the fitness of the background music that recommended by the system through interviewing the subjects.en_US
dc.language.isoen_USen_US
dc.subjectBackground music recommendation systemen_US
dc.subjectMoodsen_US
dc.subjectLatent factor modelen_US
dc.subjectRecommender systemsen_US
dc.subjectCollaborative filteringen_US
dc.subjectMultimodal information retrievalen_US
dc.titleBackground music recommendation based on latent factors and moodsen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.knosys.2018.07.001en_US
dc.identifier.journalKNOWLEDGE-BASED SYSTEMSen_US
dc.citation.volume159en_US
dc.citation.spage158en_US
dc.citation.epage170en_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:000446145600013en_US
dc.citation.woscount1en_US
顯示於類別:期刊論文