Personalized recommendations on books for K-12 readers

M. S. Pera, Yiu-Kai Ng
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引用次数: 6

Abstract

Learning to read efficiently and effectively is emphasized at the elementary and high school levels. Finding books that children/youth are interested in reading, however, is a non-trivial task due to the diversity of topics and different readability levels covered in the huge volume of books available these days. Ideally, K-12 students can turn to book recommenders which suggest books that match their interests. However, since the preferences and reading levels of these students vary from one grade to another, books suggested by existing recommenders, which ignore the literacy skills and the personal interests of their users, may be unsuitable for the targeted audience. In this paper, we present additional design issues that should be applied in developing a book recommender based on BReK12, our previously-proposed book recommender for K-12 users, to further enhance the quality of its recommendations. BReK12, which performs content and readability analysis to identify books potentially appealing to its users, is extended to incorporate (i) a multi-criteria analysis that studies its users' complex and diverse interests and (ii) an enhanced readability-detection tool that determines precisely the readability levels of books which match the literary skills of its users.
为K-12的读者提供个性化的书籍推荐
学习高效阅读在小学和高中阶段被强调。然而,寻找儿童/青少年感兴趣的书籍是一项艰巨的任务,因为如今大量的书籍涵盖了主题的多样性和不同的可读性水平。理想情况下,K-12学生可以求助于推荐符合他们兴趣的书籍的推荐人。然而,由于这些学生的偏好和阅读水平因年级而异,现有的推荐书忽视了用户的读写能力和个人兴趣,可能不适合目标受众。在本文中,我们提出了基于BReK12(我们之前为K-12用户提出的图书推荐)开发图书推荐时应应用的其他设计问题,以进一步提高其推荐质量。BReK12执行内容和可读性分析,以识别对其用户有潜在吸引力的书籍,扩展到包含(i)多标准分析,研究其用户复杂和多样化的兴趣,以及(ii)增强的可读性检测工具,精确确定与用户文学技能相匹配的书籍的可读性水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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