How good your recommender system is? A survey on evaluations in recommendation
How good your recommender system is? A survey on evaluations in recommendation
复制标题
你们的推荐系统有多好？
DOI：
10.1007/s13042-017-0762-9
复制
发表时间：
2017-12
影响因子：
5.6
通讯作者：
Ma Shaoping
中科院分区：
文献类型：
--
作者：
Silveira Thiago;Zhang Min;Lin Xiao;Liu Yiqun;Ma Shaoping
推荐系统已经成为一个非常有用的工具，为各种各样的领域。研究人员一直在尝试改进他们的算法，以便向用户发布更好的预测。然而，该领域目前的挑战之一是如何正确评估推荐系统生成的预测。在离线评价的范围内，一些传统的评价概念，如准确率，均方根误差和P@N的top-k建议进行了探讨。近年来，越来越多的研究提出了新奇、多样性和偶然性等新概念。这些概念已被解决的目标，以满足用户的要求。在以前的工作中已经提出了许多定义和指标。在缺乏结合传统评价指标和最新研究进展对推荐评价进行具体总结的情况下，本文对主要研究进行了综述和整理，提出了概念定义，并提出了评价推荐的指标或策略。此外，该调查还确定了概念之间的关系，根据其目标对它们进行分类，并建议了用户满意度的潜在未来主题。
Recommender Systems have become a very useful tool for a large variety of domains. Researchers have been attempting to improve their algorithms in order to issue better predictions to the users. However, one of the current challenges in the area refers to how to properly evaluate the predictions generated by a recommender system. In the extent of offline evaluations, some traditional concepts of evaluation have been explored, such as accuracy, Root Mean Square Error and P@N for top-k recommendations. In recent years, more research have proposed some new concepts such as novelty, diversity and serendipity. These concepts have been addressed with the goal to satisfy the users’ requirements. Numerous definitions and metrics have been proposed in previous work. On the absence of a specific summarization on evaluations of recommendation combining traditional metrics and recent progresses, this paper surveys and organizes the main research that present definitions about concepts and propose metrics or strategies to evaluate recommendations. In addition, this survey also settles the relationship between the concepts, categorizes them according to their objectives and suggests potential future topics on user satisfaction.
登录
查看更多内容
DOI：
10.1145/3041021.3054190
发表时间：
2016-10
期刊：
Proceedings of the 26th International Conference on World Wide Web Companion
影响因子：
--
作者：
Nir Rosenfeld;Y. Mansour;E. Yom-Tov
通讯作者：
Nir Rosenfeld;Y. Mansour;E. Yom-Tov
DOI：
--
发表时间：
2010
期刊：
--
影响因子：
--
作者：
Takayuki Akiyama;Kiyohiro Obara;M. Tanizaki
通讯作者：
Takayuki Akiyama;Kiyohiro Obara;M. Tanizaki
DOI：
10.1145/2959100.2959150
发表时间：
2016-09
期刊：
Proceedings of the 10th ACM Conference on Recommender Systems
影响因子：
--
作者：
Qian Zhao;Shuo Chang;F. M. Harper;J. Konstan
通讯作者：
Qian Zhao;Shuo Chang;F. M. Harper;J. Konstan
DOI：
10.1109/wi-iat.2012.135
发表时间：
2012-12
期刊：
2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
影响因子：
--
作者：
Qiuxia Lu;Tianqi Chen;Weinan Zhang;Diyi Yang;Yong Yu
通讯作者：
Qiuxia Lu;Tianqi Chen;Weinan Zhang;Diyi Yang;Yong Yu
DOI：
10.1145/2043932.2043955
发表时间：
2011-10
期刊：
--
影响因子：
--
作者：
S. Vargas;P. Castells
通讯作者：
S. Vargas;P. Castells