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| Title: | 冷起始使用者的跨領域推薦機制
Cross-Domain Recommendation Mechanism for Cold-Start Users |
| Authors: | 徐嘉泰
Hsu, Chia Tai |
| Contributors: | 沈錳坤
Shan, Man Kwan
徐嘉泰
Hsu, Chia Tai |
| Keywords: | 推薦系統
跨領域推薦系統
冷起始使用者
偏最小平方法
偏最小平方迴歸
recommender system
cross-domain recommender system
cold-start user
partial least squares
partial least squares regression |
| Date: | 2016 |
| Issue Date: | 2016-09-02 00:13:37 (UTC+8) |
| Abstract: | 推薦系統在近代的網路購物中十分常見，在多數的電子商務網站皆可見其蹤跡。推薦系統可以幫助使用者在無垠的商品海中挑選適當的商品，節省使用者的時間與精力，同時也能夠提升商品的銷售量，增加商家的獲利。跨領域推薦系統(cross-domain recommender system)是推薦系統的其中一種，對於此方面的研究漸漸興起，跨領域推薦系統主要是希望利用使用者對不同領域物品的的喜好的特性進行推薦。
冷起始使用者問題(cold-start user problem)是推薦系統中十分常見的實務問題，在使用者缺乏資訊的情況下，推薦系統難以為這些使用者推薦物品、或是造成推薦的不準確性。目前有一些跨領域推薦系統的研究，但鮮少解決跨領域推薦中的冷起始使用者問題，特別是使用者在目標領域全無資訊的情況下的困境，因此本研究提出解決此問題的方法。
本研究旨在提出一個基於相關性分析的協同式過濾(collaborative filtering)跨領域推薦模型，該模型主要解決在多重領域的情況下使用者於目標領域的冷起始問題。本研究使用PLSR(Partial Least Squares Regression)作為整個模型的核心方法，利用PLSR迴歸預測的特性預測使用者跨領域的評分(rating)。此外，過程中結合其他的推薦技術以解決PLSR無法應用於有缺值(missing value)資料的問題。本研究最主要的貢獻，在於解決推薦對象在目標領域冷起始的情況低推薦準確率的問題，實驗也證明本研究所提出的推薦方法有不錯的Mean Absolute Error與Root Mean Squared Error評估數值，特別是在資料較少的情況下還能比其他推薦方法有更佳的推薦效果。
Recommender systems are common in the E-Commerce platforms in recent years. Recommender systems are able to help users find suitable items among large amount of products. It can save users’ time and increase profits of sellers. Cross-domain recommender systems, which related researches are blooming recently, is a sort of recommender systems that recommend items based on users’ different taste across domains.
Cold-start users problem is a common practical problem in recommender systems. In the case of scarce information of users, it is difficult for recommender systems to recommend items or even lead to inaccuracy of recommendation results. There are few studies on dealing with cold-start users problem in cross-domain recommender systems, especially in the case of shortage of information.
This thesis proposes a collaborative filtering cross-domain recommendation model based on correlation analysis, which mainly solve the cold-start problem that users have no preference information in the target domain in cross-domain situation. The proposed recommendation model is able to predict the rating among multiple domains for cold start users based on the Partial Least Squares Regression (PLSR). The proposed model incorporates other recommendation techniques for dealing with the problem that PLSR cannot use the data with missing values as input. The primary contribution of this research is that the proposed model is able to solve the problem of low recommendation accuracy for cold-start users in target domain. The experiment shows Mean Absolute Error and Root Mean Squared Error of the proposed model are better than the other approaches when data is sparse. |
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| Description: | 碩士
國立政治大學
資訊科學學系
101753027 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0101753027 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系] 學位論文
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