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A cross-language personalized recommendation model in digital libraries

机译:数字图书馆中的跨语言个性化推荐模型

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Purpose - The purpose of this paper is to develop a cross-language personalized recommendation model based on web log mining, which can recommend academic articles, in different languages, to users according to their demands. Design/methodology/approach - The proposed model takes advantage of web log data archived in digital libraries and learns user profiles by means of integration analysis of a user's multiple online behaviors. Moreover, keyword translation was carried out to eliminate language dissimilarity between user and item profiles. Finally, article recommendation can be achieved using various existing algorithms. Findings - The proposed model can recommend articles in different languages to users according to their demands, and the integration analysis of multiple online behaviors can help to better understand a user's interests. Practical implications - This study has a significant implication for digital libraries in non-English countries, since English is the most popular language in current academic articles and it is a very common phenomenon for users in these countries to obtain literatures presented by more than one language. Furthermore, this approach is also useful for other text-based item recommendation systems. Originality/value - A lot of research work has been done in the personalized recommendation area, but few works have discussed the recommendation problem under multiple linguistic circumstances. This paper deals with cross-language recommendation and, moreover, the proposed model puts forward an integration analysis method based on multiple online behaviors to understand users' interests, which can provide references for other recommendation systems in the digital age.
机译:目的-本文的目的是开发一种基于Web日志挖掘的跨语言个性化推荐模型,该模型可以根据用户的需求向用户推荐不同语言的学术文章。设计/方法/方法-提出的模型利用了存储在数字图书馆中的Web日志数据,并通过对用户多种在线行为的集成分析来学习用户资料。此外,还进行了关键字翻译,以消除用户和项目资料之间的语言差异。最后,可以使用各种现有算法来实现文章推荐。调查结果-提出的模型可以根据用户的需求向他们推荐不同语言的文章,并且对多种在线行为的整合分析可以帮助更好地了解用户的兴趣。实际意义-这项研究对非英语国家的数字图书馆具有重要意义,因为英语是当前学术文章中最受欢迎的语言,并且对于这些国家/地区的用户来说,获得由多种语言呈现的文献是一种非常普遍的现象。此外,该方法对于其他基于文本的项目推荐系统也很有用。原创性/价值-在个性化推荐区域中已经进行了很多研究工作,但是很少有人讨论多种语言环境下的推荐问题。本文针对跨语言推荐,提出了一种基于多种在线行为的综合分析方法,以了解用户的兴趣,为数字时代的其他推荐系统提供参考。

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