Recommendation of Similar Users in Social Resource Sharing Websites

• Main Authors: Pao-Yuan ChangChien, 張簡堡元
• Other Authors: Cheng-Lung Huang
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/70093974864027160729

碩士 === 國立高雄第一科技大學 === 資訊管理研究所 === 100 === The label function of Web 2.0 Folksonomy enables social resource sharing websites to become a crucial platform for managing and sharing knowledge over the Internet. Initially, social resource sharing websites simply provided bookmark collection and classification functions, the social networking function of “friends” was added recently. A number of websites even classify friends further into “follower” or “following” categories. This change makes social resource sharing websites knowledge sharing websites with socializing functions instead of simply enabling users to manage or share collected bookmarks. A new collaborative recommendation model was designed after the social networking function was added. Therefore, this study proposes a user similarity calculation index based on the user label information and establishes a collaborative recommendation model. Subsequently, we explore whether the two factors, that is, friend dimensions and friend layers, cause a differing recommendation model effect. The recommendation method includes the following steps: (1) Establish item and user profile; (2) locate similar friends in the first layer of friends in the network; (3) locate similar users that have not established direct friendships with the target users from the first layer of similar friends; and (4) recommend similar users from the previous layers to the target users. This study uses the Web 2.0 social bookmarking website Diigo as an example and presents the recommendation procedure and the effects of recommending similar users to target users according to the user information collected by the website. The results of surveying 30 participants indicated the following: (1) The effect of the recommendation model developed in this study is superior to that of popularity recommendations; (2) the recommendation sequence obtained by the index calculation of the recommendation model developed in this study is appropriate; and (3) users can establish relationships with their friends in the “following” category. Therefore, the effect of recommending similar users from the “following” category is superior to users passively accepting users recommended from the “follower” category.