Recommending trade exhibitions by integrating semantic information with collaborative filtering

Xuetao Guo, Jie Lu
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引用次数: 3

Abstract

Recommender systems have gained successfully applications particular in e-commerce domain. However, existing recommendation approaches can not effectively deal with recommendation issue of one-and-only items occurred in government-to-business services, e.g. recommendation of trade exhibitions. Thus, in this study, we propose a novel approach by integrating semantic information with the traditional item-based collaborative filtering, and attempt to help the businesses choose the right trade exhibitions at the right time. The outcome of this study have tremendous significance in overcoming the 'new item' problem of existing recommendation approaches.
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将语义信息与协同过滤相结合进行展会推荐
推荐系统在电子商务领域获得了成功的应用。然而,现有的推荐方法并不能有效地处理政府对企业服务中出现的一次性项目的推荐问题,例如贸易展览的推荐。因此,在本研究中,我们提出了一种新的方法,将语义信息与传统的基于项目的协同过滤相结合,并试图帮助企业在合适的时间选择合适的贸易展览。本研究的结果对于克服现有推荐方法的“新项目”问题具有重要意义。
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