Predicting the popularity of Web 2.0 items based on user comments

• Main Authors: HE, Xiangnan, Gao, Ming, KAN, Min-Yen, LIU, Yiqun, SUGIYAMA, Kazunari
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Popularity Prediction; Item Ranking; Bipartite Graph Ranking; Comments Mining; BUIR; Databases and Information Systems; Numerical Analysis and Scientific Computing; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/4228; https://ink.library.smu.edu.sg/context/sis_research/article/5231/viewcontent/sigir2014_he.pdf
• Institution: Singapore Management University

In the current Web 2.0 era, the popularity of Web resources fluctuates ephemerally, based on trends and social interest. As a result, content-based relevance signals are insufficient to meet users' constantly evolving information needs in searching for Web 2.0 items. Incorporating future popula...