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Putting the collaborator back into collaborative filtering

机译:将协作者放回协作式过滤

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摘要

Most of the published approaches to collaborative filtering and recommender systems concentrate on mathematical approaches for identifying user / item preferences. This paper demonstrates that by considering the psychological decision making processes that are being undertaken by the users of the system it is possible to achieve a significant improvement in results. This approach is applied to the Netflix dataset and it is demonstrated that it is possible to achieve a score better than the Cinematch score set at the beginning of the Netflix competition without even considering individual preferences for individual movies. The result has important implications for both the design and the analysis of the data from collaborative filtering systems.
机译:协作过滤和推荐系统的大多数已发布方法都集中在识别用户/项目偏好的数学方法上。本文证明,通过考虑系统用户正在执行的心理决策过程,可以显着改善结果。该方法已应用于Netflix数据集,并且证明了即使不考虑个人对单个电影的偏好,也可以获得比Netflix竞赛开始时设定的Cinematch分数更好的分数。结果对于协同过滤系统中数据的设计和分析都具有重要意义。

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