於個人化社交網路中發掘未知但感興趣之物件推薦系統

Discovering Unknown But Interesting Items on Personal Social Network

段莊麟(Juang-Lin Duan)

指導教授 ： 黃仁暐

元智大學/資訊學院/資訊工程學系/碩士(2012年)

摘要

近年來社交網路越來越流行與蓬勃，許許多多的推薦系統都與社交網路做結合。傳統的推薦系統總是專注在提供熱門的物件或朋友所推薦的物件。雖是常見的推薦方式，但會有下述幾個問題。熱門的物件總是會佔據在推薦清單的前幾名，而且使用者通常都已經知道這些物件。此外，傳統的推薦系統會根據目標使用者與其熟悉的使用者交流的次數來推薦物件，但是目標使用者可能不感興趣。還有往往會忽略熱門度較低但是跟使用者相似且有趣的物件。本文中我們提出UbiMiner演算法，來發掘未知但感興趣之物件。我們提出了三個分數，熱門度分數、社交行為分數和使用者相似分數，並將運用在使用者個人的社交網路上。將這三個分數做結合即可產生出推薦清單給使用者。最後，實驗的結果也顯示UbiMiner推薦未知但感興趣之物件表現的比傳統推薦系統好。

關鍵字

推薦 ； 社交網路 ； 未知但感興趣

並列摘要

Social networking service has become very popular recently. Many recommendation systems have been proposed to integrate with social networking websites. Traditional recommendation systems focus on providing popular items or items posted by close friends. This strategy causes some problems. Popular items always occupy the recommendation list and they are usually already known by the user. In addition, items recommended by familiar users, who frequently communicate with the target user, may not be interesting. Moreover, interesting items from similar users with lower popularity are ignored. In this paper, we propose an algorithm, UbiMiner, to discover unknown but interesting items. We propose three scores, i.e., Quartile-aided Popularity Score, Social Behavior Score, and User Similarity Score, to model the popularity of items, the familiarity of friends, and the similarity of users respectively in the target user's personal social network. Combining these three scores, the recommendation list containing unknown but interesting items can be generated. Experimental results show that UbiMiner outperforms traditional methods in terms of the percentages of unknown and interesting items in the recommendation list.

並列關鍵字

Recommendation ； social networks ； unknown but interesting

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