论文标题
研究人员如何无需操作自己的推荐系统即可快速，廉价的用户反馈
How Researchers Could Obtain Quick and Cheap User Feedback on their Algorithms Without Having to Operate their Own Recommender System
论文作者
论文摘要
大多数建议算法是根据历史基准数据集评估的。对历史基准数据集进行评估的行为既快速又便宜，但不包括实际消费建议的用户的观点。很少收集用户反馈，因为它需要访问操作推荐系统。建立和维护运营推荐系统会及时，经济负担，大多数研究人员无法承受。我们旨在减轻这种负担，以促进对建议算法的广泛以用户为中心的评估，尤其是该领域的新手研究人员。我们在评估工具上介绍了正在进行的工作，该评估工具实现了一种新型范式，该范式可以以用户为中心的建议算法评估，而无需访问操作推荐系统。最后，我们绘制计划在评估工具的帮助下进行的实验。
The majority of recommendation algorithms are evaluated on the basis of historic benchmark datasets. Evaluation on historic benchmark datasets is quick and cheap to conduct, yet excludes the viewpoint of users who actually consume recommendations. User feedback is seldom collected, since it requires access to an operational recommender system. Establishing and maintaining an operational recommender system imposes a timely and financial burden that a majority of researchers cannot shoulder. We aim to reduce this burden in order to promote widespread user-centric evaluations of recommendation algorithms, in particular for novice researchers in the field. We present work in progress on an evaluation tool that implements a novel paradigm that enables user-centric evaluations of recommendation algorithms without access to an operational recommender system. Finally, we sketch the experiments we plan to conduct with the help of the evaluation tool.
