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Xiaolong (Luke) Zhang

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Publications by Xiaolong (Luke) Zhang (bibliography)

2011

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Shi, Pan, Xu, Heng and Zhang, Xiaolong (Luke) (2011): Informing security indicator design in web browsers. In: Proceedings of the 2011 iConference 2011. pp. 569-575.

“In this paper, we aim at providing conceptual and empirical insights to the design of security indicators in web browsers. In examining why security indicators in web browsers fail to warn users about web frauds, we propose affordance-based principles for our new design of web authentication indicators. Following these principles, we present a new design for Extended Validation (EV) certificate interface in the Firefox browser. We then conduct an exploratory qualitative study to evaluate three different versions of EV indicators. Our findings offer some preliminary implications for the designs of more effective web authentication indicators.”

2010

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Gou, Liang, Zhang, Xiaolong (Luke), Chen, Hung-Hsuan, Kim, Jung-Hyun and Giles, C. Lee (2010): Social network document ranking. In: JCDL10 Proceedings of the 2010 Joint International Conference on Digital Libraries 2010. pp. 313-322.

“In search engines, ranking algorithms measure the importance and relevance of documents mainly based on the contents and relationships between documents. User attributes are usually not considered in ranking. This user-neutral approach, however, may not meet the diverse interests of users, who may demand different documents even with the same queries. To satisfy this need for more personalized ranking, we propose a ranking framework. Social Network Document Rank (SNDocRank), that considers both document contents and the relationship between a searched and document owners in a social network. This method combined the traditional tf-idf ranking for document contents with out Multi-level Actor Similarity (MAS) algorithm to measure to what extent document owners and the searcher are structurally similar in a social network. We implemented our ranking method in simulated video social network based on data extracted from YouTube and tested its effectiveness on video search. The results show that compared with the traditional ranking method like tf-idfs the SNDocRank algorithm returns more relevant documents. More specifically, a searcher can get significantly better results be being in a larger social network, having more friends, and being associated with larger local communities in a social network.”

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Gou, Liang, Chen, Hung-Hsuan, Kim, Jung-Hyun, Zhang, Xiaolong (Luke) and Giles, C. Lee (2010): SNDocRank: document ranking based on social networks. In: Proceedings of the 2010 International Conference on the World Wide Web 2010. pp. 1103-1104.

“To improve the search results for socially-connect users, we propose a ranking framework, Social Network Document Rank (SNDocRank). This framework considers both document contents and the similarity between a searcher and document owners in a social network and uses a Multi-level Actor Similarity (MAS) algorithm to efficiently calculate user similarity in a social network. Our experiment results based on YouTube data show that compared with the tf-idf algorithm, the SNDocRank method returns more relevant documents of interest. Our findings suggest that in this framework, a searcher can improve search by joining larger social networks, having more friends, and connecting larger local communities in a social network.”

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