博碩士論文 105525009 完整後設資料紀錄

DC 欄位 語言
DC.contributor軟體工程研究所zh_TW
DC.creator陳璞zh_TW
DC.creatorPu Chenen_US
dc.date.accessioned2018-7-6T07:39:07Z
dc.date.available2018-7-6T07:39:07Z
dc.date.issued2018
dc.identifier.urihttp://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=105525009
dc.contributor.department軟體工程研究所zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstract隨著網際網路的發展,人們面臨越來越多的選擇,例如購物網站中商品的選擇,影音網站中該看哪些影片的選擇。推薦系統在這些網站中扮演幫人們迅速決定的重要角色。在這篇論文中,我們針對在推薦系統中的知名方法潛在因子模型 (Latent factor model)進行分析與改良,並提出了加權潛在因子模型和多項式潛在因子模型。這兩個模型分別賦予了傳統潛在因子模型權重參數和非線性的特徵組合。我們將這兩個模型對五種開放資料集進行了許多實驗,發現相較於傳統模型,這兩個模型能夠有更好地預測效果。由於我們提出的模型是基於潛在因子模型的變體,我們的模型也可以應用於其他潛在因子模型上,如SVD++模型和NMF模型。zh_TW
dc.description.abstractWith the development of the Internet, people are faced with more and more choices, such as the choice of products in shopping websites and the choice of which videos to watch in video and audio websites. The recommendation system plays an important role in these sites to help people decide quickly. In this paper, we analyze the well known method -- the latent factor model in the recommendation system, and propose the weighted latent factor model and the polynomial latent factor model. These two models respectively give the traditional latent factor model weights and nonlinear feature combinations. We conducted many experiments on these two models for the five open data sets and found that the two models have better predictive effects than the traditional models. Since our proposed model is based on the latent factor models, our model can also be applied to other latent factor models such as SVD++ model and NMF model.en_US
DC.subject推薦系統zh_TW
DC.subjectSVD模型zh_TW
DC.subject矩陣分解模型zh_TW
DC.subjectrecommender systemen_US
DC.subjectSVD modelen_US
DC.subjectmatrix factorization methoden_US
DC.title基於SVD模型之變形 - WSVD 與 PSVDzh_TW
dc.language.isozh-TWzh-TW
DC.titleVariants of the SVD model - WSVD and PSVDen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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