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A Method to Maintain Item Recommendation Equality Among Equivalent Items in Recommender Systems

机译:一种维护推荐系统中等效项之间的项目推荐平等的方法

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Collaborative Filtering is a useful algorithm to offer personalized recommendations for users. However, there are several technical challenges in collaborative filtering, including the first-rater problem, where an item not yet evaluated cannot be recommended until it has been rated. In the paper, the presenting method deals with the first-rater problem that is similar to the process starvation is operating systems. The method reduces the score gap between items and makes it possible for a new item or an item with no user preference to be recommended automatically. Thus, the system can recommend items in the same group without bias. Finally, we present an analysis of an example of the algorithm.
机译:协作过滤是一个有用的算法,为用户提供个性化建议。然而,在协作过滤中存在几种技术挑战,包括第一次rater问题,其中尚未评估的项目,直到它被评为。在本文中,呈现方法涉及类似于过程饥饿的第一级问题是操作系统。该方法减少了项目之间的分数差距,并使新项目或项目可以自动建议使用,而没有用户偏好。因此,系统可以在没有偏见的情况下推荐同一组中的项目。最后,我们对算法的示例进行了分析。

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