Decayed DivRank: capturing relevance, diversity and prestige in information networks

Authors:
Pan Du;Jiafeng Guo;Xue-Qi Cheng
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Venue:
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Year:
2011

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Abstract

Many network-based ranking approaches have been proposed to rank objects according to different criteria, including relevance, prestige and diversity. However, existing approaches either only aim at one or two of the criteria, or handle them with additional heuristics in multiple steps. Inspired by DivRank, we propose a unified ranking model, Decayed DivRank (DDRank), to meet the three criteria simultaneously. Empirical experiments on paper citation network show that DDRank can outperform existing algorithms in capturing relevance, diversity and prestige simultaneously in ranking.