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首页> 外文会议>IEEE International Conference of Safe Production and Informatization >Case-based Reasoning for Resolving the Diversity-accuracy Dilemma of Recommendation System through Subdivision
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Case-based Reasoning for Resolving the Diversity-accuracy Dilemma of Recommendation System through Subdivision

机译：基于案例的推理，通过细分解决推荐系统的多样性准确性困境

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摘要

Recommendation systems are widely used to help their netizens find what they want from cyber space and many various applications have been developed in different areas like news feeding, entree recommending and online shopping, etc. Many researches have found that their system performance is obsession with diversity-accuracy dilemma, and recommender has to balance recommendation sequence results. CBR-recommender is suggested for it has a comprehensive expression of human sense, logics and result explanation, and has more system flexibility to integrate many AI tools in dealing with such diversity-accuracy dilemma. Covering algorithm is also proposed to dynamically cluster similar users or items as subdomain for which all users are dynamitic divided into multiple clusters according to a certain criterion. Our experiments results indicate that users with similar hobbies are assigned to the same sub-domain as many specific sub-classes, and the refined classification results are more conducive for the problem-solving.
机译：推荐系统广泛用于帮助他们的网民从网络空间中找到他们想要的东西，并且许多各种应用程序已经在新闻喂养，进入的推荐和在线购物等不同领域开发的。许多研究发现他们的系统性能是对多样性的痴迷-Accuracy困境，推荐人必须平衡推荐序列结果。建议CBR推荐人为人类感觉，逻辑和结果解释具有全面表达，并具有更多的系统灵活性，可以集成许多AI工具，以处理这种多样性准确性困境。还提出了覆盖算法，以将类似的用户或物品动态集群，作为根据某个标准将所有用户的散文分成多个群集的子域。我们的实验结果表明，具有类似爱好的用户被分配给与许多特定子类相同的子域，并且精制的分类结果更有利于解决问题。

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