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Accurate and Diverse Recommendations via Integrated Communities of Interest and Trustable Neighbors

机译:通过综合利益社区和可信赖的邻居提供准确多样的建议

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摘要

Considering the users' complete spectrum of interests, the limitation of current research on recommender systems lies in that they have only paid attention to improving the accuracy of recommendation algorithms while neglected the diversification of recommendations. In this paper, we integrated a user preference matching algorithm based on communities of interests and a diverse information recommendation algorithm based on trustable neighbors to develop a hybrid information recommendation model that allows for both accuracy and diversity. Results of experiment and evaluation indicated this model can increase the diversity of recommendations with only a minimal accuracy loss.
机译:考虑到用户的全部兴趣范围,当前对推荐系统的研究的局限性在于,他们仅关注提高推荐算法的准确性,而忽略了推荐的多样化。在本文中,我们集成了基于兴趣社区的用户偏好匹配算法和基于可信赖邻居的多样化信息推荐算法,以开发一种兼顾准确性和多样性的混合信息推荐模型。实验和评估结果表明,该模型可以以最小的准确性损失来增加建议的多样性。

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