论文标题
基于张量的协作过滤具有平滑评级量表
Tensor-based Collaborative Filtering With Smooth Ratings Scale
论文作者
论文摘要
常规的协作过滤技术没有考虑到用户评级感知中差异的影响。一些用户可能很少为物品提供5星，而另一些用户几乎总是为所选项目分配5星。即使他们在相同的项目中具有相同的经验，在其评估方式中，这种系统的差异也会导致推荐系统有效从数据中提取正确模式的能力的系统错误。为了减轻此问题，我们介绍了评级的相似性矩阵，该矩阵代表了种群水平上不同评级值之间的依赖性。因此，如果平均而言，评级之间存在相关性，则可以通过偏离降低或转移用户费率的效果来提高建议建议的质量。
Conventional collaborative filtering techniques don't take into consideration the effect of discrepancy in users' rating perception. Some users may rarely give 5 stars to items while others almost always assign 5 stars to the chosen item. Even if they had experience with the same items this systematic discrepancy in their evaluation style will lead to the systematic errors in the ability of recommender system to effectively extract right patterns from data. To mitigate this problem we introduce the ratings' similarity matrix which represents the dependency between different values of ratings on the population level. Hence, if on average the correlations between ratings exist, it is possible to improve the quality of proposed recommendations by off-setting the effect of either shifted down or shifted up users' rates.
