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A novel hybrid approach improving effectiveness of recommender systems

机译：一种新颖的混合方法，可提高推荐系统的效率

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摘要

Recommender systems support users by generating potentially interesting suggestions about relevant products and information. The increasing attention towards such tools is witnessed by both the great number of powerful and sophisticated recommender algorithms developed in recent years and their adoption in many popular Web platforms. However, performances of recommender systems can be affected by many critical issues as for instance, over-specialization, attribute selection and scalability. To mitigate some of such negative effects, a hybrid recommender system, called Relevance Based Recommender, is proposed in this paper. It exploits individual measures of perceived relevance computed by each user for each instance of interest and, to obtain a better precision, also by considering the analogous measures computed by the other users for the same instances. Some experiments show the advantages introduced by this recommender when generating potentially attractive suggestions.
机译：推荐系统通过生成有关相关产品和信息的潜在有趣建议来支持用户。近年来开发的大量强大而复杂的推荐算法及其在许多流行的Web平台中的采用见证了对此类工具的日益关注。但是，推荐系统的性能可能会受到许多关键问题的影响，例如过度专业化，属性选择和可伸缩性。为了减轻这种负面影响，本文提出了一种混合推荐系统，称为基于相关性的推荐器。它利用每个用户为每个感兴趣实例计算的感知相关性的单独度量，并通过考虑其他用户为相同实例计算的相似度量来获得更好的精度。一些实验表明，在生成潜在有吸引力的建议时，此推荐程序会带来很多好处。

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