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首页> 外文期刊>Knowledge and Data Engineering, IEEE Transactions on >Ranking and Suggesting Popular Items
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Ranking and Suggesting Popular Items

机译：热门商品排名和建议

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摘要

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.
机译：我们考虑对项目受欢迎程度进行排名并根据用户反馈建议受欢迎项目的问题。通过反复呈现一组建议项，以及用户根据自己的偏好从该建议集或所有可能项的集合中选择项，可以获得用户反馈。目的是快速了解商品的真实受欢迎程度（不受所提出的建议的偏见），并提出真实的热门商品。困难在于，向用户提出建议可能会增强某些商品的受欢迎程度，并扭曲最终的商品排名。所描述的对项目进行排序和建议的问题出现在包括针对社会标签系统的搜索查询建议和标签建议的各种应用中。我们提出并研究了几种用于对热门项目进行排名和建议的算法，提供了对它们的性能的分析结果，并提供了使用从流行的社会图书标记服务的为期一个月的爬网推断出的标签受欢迎程度获得的数值结果。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提供良好的性能。

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