Cross-lingual keyword recommendation using latent topics
Cross-lingual keyword recommendation using latent topics
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DOI：
10.1145/1869446.1869454
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发表时间：
2010-09
期刊：
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通讯作者：
A. Takasu
中科院分区：
文献类型：
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作者：
A. Takasu
多语言文本处理对于基于内容的推荐系统和混合推荐系统都是非常重要的。它帮助推荐系统从更广泛的来源中提取内容信息。它还使系统能够以用户的母语推荐项目。本文提出了一种基于扩展的潜在Dirichlet分配模型的跨语言关键词推荐方法，用于从并行语料库中提取潜在特征。通过该模型，该方法可以从不同语言的文本中推荐关键字。我们评估所提出的方法使用跨语言的书目数据库，其中包含英语和日语的摘要和关键字，并表明所提出的方法可以推荐关键字摘要在跨语言的环境中，几乎相同的准确性，在单语环境。
Multi-lingual text processing is important for content-based and hybrid recommender systems. It helps recommender systems extract content information from broader sources. It also enables systems to recommend items in a user's native language. We propose a cross-lingual keyword recommendation method, which is built on an extended latent Dirichlet allocation model, for extracting latent features from parallel corpora. With this model, the proposed method can recommend keywords from text written in different languages. We evaluate the proposed method using a cross-lingual bibliographic database that contains both English and Japanese abstracts and keywords and show that the proposed method can recommend keywords from abstracts in a cross-lingual environment with almost the same accuracy as in a monolingual environment.