期刊名称：International Journal of Computer Science & Information Technology (IJCSIT)
印刷版ISSN：0975-4660
电子版ISSN：0975-3826
出版年度：2012
卷号：4
期号：1
页码：147
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：The problem of ranking popular items is getting increasing interest from a number of research areas.Several algorithms have been proposed for this task. The described problem of ranking and suggestingitems arises in diverse applications include interactive computational system for helping people toleverage social information; in technical these systems are called social navigation systems. These socialnavigation systems help each individual in their performance and decision making over selecting theitems. Based on the each individual response the ranking and suggesting of popular items were done. Theindividual feedback might be obtained by displaying a set of suggested items, where the selection of itemsis based on the preference of the individual. The aim is to suggest popular items by rapidly studying thetrue popularity ranking of items. The difficulty in suggesting the true popular items to the users can giveemphasis to reputation for some items but may mutilate the resulting item ranking for other items. So theproblem of ranking and suggesting items affected many applications including suggestions and searchquery suggestions for social tagging systems. In this paper we propose Naïve Bayes algorithm forranking and suggesting popular items.
关键词：Label ranking; suggesting; computational systems; collaborative filtering; preferential;attachment; mutilate; true popular item sets; tagging systems; suggested itemsn and ranking;rules
