The primary objective of this paper is to present an approach for recommender systems that can assimilate ranking to the voters or rankers so that recommendation can be made by giving priority to experts suggestion over usual recommendation. To accomplish this, we have incorporated the concept of human-centric aggregation via Ordered Weighted Aggregation (OWA). Here, we are advocating ranked recommendation where rankers are assigned weights according to their place in the ranking. Further, the recommendation process which is presented here for the recommendation of books to university students exploits linguistic data summaries and Ordered Weighted Aggregation (OWA) technique. In the suggested approach, the weights are assigned in a way that it associates higher weights to best ranked university. The approach has been evaluated over eight different parameters. The superiority of the proposed approach is evident from the evaluation results. We claim that proposed scheme saves storage spaces required in traditional recommender systems as well as it does not need users prior preferences and hence produce a solution for cold start problem. This envisaged that the proposed scheme can be very useful in decision making problems, especially for recommender systems. In addition, it emphasizes on how human-centric aggregation can be useful in recommendation researches, and also it gives a new direction about how various human specific tasks can be numerically aggregated.

中文翻译：

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我们可以汇总人类智力吗？一种使用有序加权平均算子的以人为中心的聚合方法

本文的主要目的是为推荐系统提供一种方法，该方法可以使选民或等级同化排名，从而可以通过将专家建议优先于常规建议来进行推荐。为此，我们通过有序加权聚合（OWA）引入了以人为中心的聚合概念。在这里，我们提倡排名推荐，其中根据排名在排名中为排名分配权重。此外，此处介绍的推荐过程是利用语言数据摘要和有序加权聚合（OWA）技术向大学生推荐书籍。在建议的方法中，权重的分配方式是将较高的权重与排名最高的大学联系在一起。该方法已通过八个不同参数进行了评估。从评估结果中可以明显看出该方法的优越性。我们声称，所提出的方案节省了传统推荐系统中所需的存储空间，并且不需要用户事先的偏好，因此可以解决冷启动问题。可以设想，所提出的方案在决策问题中非常有用，特别是对于推荐系统。此外，它强调了以人为中心的聚合如何在推荐研究中有用，并且为如何以数字方式聚合各种特定于人类的任务提供了新的方向。我们声称，所提出的方案节省了传统推荐系统中所需的存储空间，并且不需要用户事先的偏好，因此可以解决冷启动问题。可以设想，所提出的方案在决策问题中非常有用，特别是对于推荐系统。此外，它强调了以人为中心的聚合如何在推荐研究中有用，并且为如何以数字方式聚合各种特定于人类的任务提供了新的方向。我们声称，所提出的方案节省了传统推荐系统中所需的存储空间，并且不需要用户事先的偏好，因此可以解决冷启动问题。可以设想，所提出的方案在决策问题中非常有用，特别是对于推荐系统。此外，它强调了以人为中心的聚合如何在推荐研究中有用，并且为如何以数字方式聚合各种特定于人类的任务提供了新的方向。