A personalized authoritative user-based recommendation for social tagging

Abstract Personalized information recommendation based on social tagging is a hot issue in academia nowadays, butthe concept of an authoritative user has not been emphasized in the existing literature. This paper first proposes a method to determine user authority in a social tagging system, in which the quality authority and quantity authority of users are calculated from a user co-occurrence network, which is derived from users’ participation in the social tagging system. Degree centrality is employed for the user authority calculations, which are taken as weights for tag voting. On this basis, a resource model is constructed by summing up the tags from each user and their corresponding weights to represent each resource in the collection. User models are then obtained based on the resource models, and cosine similarity is used for making resource recommendations to users. An experiment was conducted on a dataset crawled from Delicious.com. The results show that the average GP relevance of the authoritative user based algorithm reaches 0.6115 much better than two benchmark algorithms.

[1]  Sonja Špiranec,et al.  The 2 nd International Conference on Integrated Information Experts vs . novices tagging behavior : an exploratory analysis , 2013 .

[2]  Seungbin Moon,et al.  Security Techniques for Prevention of Rank Manipulation in Social Tagging Services including Robotic Domains , 2014, TheScientificWorldJournal.

[3]  Yo-Sub Han,et al.  Representative reviewers for Internet social media , 2013, Expert Syst. Appl..

[4]  Christopher C. Yang,et al.  Ranking User Influence in Healthcare Social Media , 2012, TIST.

[5]  Seung-won Hwang,et al.  Ranking with tagging as quality indicators , 2008, SAC '08.

[6]  Anton Borg,et al.  Finding Influential Users in Social Media Using Association Rule Learning , 2016, Entropy.

[7]  Shrish Verma,et al.  Selecting Best Answer: An Empirical Analysis on Community Question Answering Sites , 2016, IEEE Access.

[8]  Catherine Faron-Zucker,et al.  Personalizing and Improving Resource Recommendation by Analyzing Users Preferences in Social Tagging Activities , 2017, Comput. Informatics.

[9]  Satoshi Nakamura,et al.  Towards Improving Web Search by Utilizing Social Bookmarks , 2007, ICWE.

[10]  Yanfei Xu,et al.  A Personalized Tag-Based Recommendation in Social Web Systems , 2012, AP WEB 2.0@UMAP.

[11]  Harris Wu,et al.  Harvesting social knowledge from folksonomies , 2006, HYPERTEXT '06.

[12]  Daniel Dajun Zeng,et al.  A Random Walk Model for Item Recommendation in Social Tagging Systems , 2013, TMIS.

[13]  Lin Cui,et al.  Question Recommendation Mechanism under Q&A Community based onLDA Model , 2014 .

[14]  K R Venugopal,et al.  Trust Aware System for Social Networks: A Comprehensive Survey , 2017 .

[15]  Ming Yi,et al.  Profiling users with tag networks in diffusion-based personalized recommendation , 2016, J. Inf. Sci..

[16]  Xueming Qian,et al.  Tag-Based Image Search by Social Re-ranking , 2016, IEEE Transactions on Multimedia.

[17]  Yo-Sub Han,et al.  Computing User Reputation in a Social Network of Web 2.0 , 2012, Comput. Informatics.