Ranking and Suggesting Popular Items
Ranking and Suggesting Popular Items
复制标题
DOI：
10.1109/tkde.2009.34
复制
发表时间：
2009-08
影响因子：
8.9
通讯作者：
M. Vojnović;J. Cruise;Dinan Gunawardena;P. Marbach
中科院分区：
文献类型：
--
作者：
M. Vojnović;J. Cruise;Dinan Gunawardena;P. Marbach
我们考虑的问题，排名的流行项目，并建议流行的项目，根据用户的反馈。通过迭代地呈现一组建议项目，并且用户基于他们自己的偏好从该建议集合或从所有可能项目的集合中选择项目，来获得用户反馈。我们的目标是快速了解项目的真实受欢迎程度排名（不受建议的影响），并建议真正受欢迎的项目。困难在于，向用户提出建议可能会加强某些项目的受欢迎程度，并扭曲最终的项目排名。所描述的对项目进行排名和建议的问题出现在各种应用中，包括搜索查询建议和用于社交标记系统的标记建议。我们提出并研究了几种算法的排名和建议流行的项目，其性能提供分析结果，并提出了使用推断流行的标签从一个月的爬行流行的社会图书标记服务获得的数值结果。我们的结果表明，不需要特殊配置参数的轻量级、随机更新规则可以提供良好的性能。
We consider the problem of ranking the popularity of items and suggesting popular items based on user feedback. User feedback is obtained by iteratively presenting a set of suggested items, and users selecting items based on their own preferences either from this suggestion set or from the set of all possible items. The goal is to quickly learn the true popularity ranking of items (unbiased by the made suggestions), and suggest true popular items. The difficulty is that making suggestions to users can reinforce popularity of some items and distort the resulting item ranking. The described problem of ranking and suggesting items arises in diverse applications including search query suggestions and tag suggestions for social tagging systems. We propose and study several algorithms for ranking and suggesting popular items, provide analytical results on their performance, and present numerical results obtained using the inferred popularity of tags from a month-long crawl of a popular social book marking service. Our results suggest that lightweight, randomized update rules that require no special configuration parameters provide good performance.