International Business Matching Using Word Embedding
International Business Matching Using Word Embedding
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DOI：
10.1007/978-3-319-54472-4_18
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发表时间：
2017-04
期刊：
影响因子：
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通讯作者：
Didier Gohourou;Daiki Kurita;K. Kuwabara;Hung-Hsuan Huang
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文献类型：
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作者：
Didier Gohourou;Daiki Kurita;K. Kuwabara;Hung-Hsuan Huang
推荐系统帮助用户发现他们可能需要的信息或知识，而不要求他们拥有特定的先前知识，在我们这个信息过载的时代越来越受欢迎。此外，自然语言处理技术（如单词嵌入）为从大量文本数据中提取信息提供了新的可能性。这项工作探讨了应用词嵌入作为推荐系统的基础，以帮助国际企业确定合适的同行为他们的活动的可能性。在本文中，我们描述了我们的系统，并报告初步实验使用维基百科作为语料库。我们的实验试图提供答案，以支持业务决策者，当他们正在考虑进入一个相对未知的市场，并寻求更好的理解或适当的合作伙伴。我们的实验显示了有希望的结果，这将为未来的研究铺平道路。
Recommender systems, which help users discover information or knowledge they might need without requiring them to have specific previous knowledge, are gaining popularity in our age of information overload. In addition, natural language processing techniques like word embedding offer new possibilities for extracting information from a massive amount of text data. This work explores the possibility of applying word embedding as the foundation for a recommender system to help international businesses identify appropriate counterparts for their activities. In this paper, we describe our system and report preliminary experiments using Wikipedia as a corpus. Our experiments attempt to provide answers to support business decision makers when they are considering entering a relatively unknown market and are seeking better understanding or appropriate partners. Our experiment shows promising results that will pave the way for future research.