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A Fuzzy-Based Personalized Recommender System for Local Businesses

机译:基于模糊的本地企业个性化推荐系统

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摘要

On-line reviewing systems have become prevalent in our society. User-provided reviews of local businesses have provided rich information in terms of users' preferences regarding businesses and their interactions in reviewing systems; however, little is known about how the reviewing behaviors of users can benefit businesses in terms of suggesting potential collaboration opportunities. In the current study, we aim to build a recommendation system for businesses to provide suggestions for business collaboration. Based on historical data from Yelp that shows two businesses being reviewed by the same users within a same season, we were able to identify businesses that might attract the same customers in the future, and hence provide them with a collaboration suggestion. Our results suggest that the evidence - two businesses sharing reviews from same users - can provide recommendations for businesses to pursue future collaborative marketing opportunities.
机译:在线审查系统已经在我们的社会中流行。用户对本地企业的评论提供了有关用户对企业及其在评论系统中的互动的偏好的丰富信息;但是,对于用户的审查行为如何从潜在的合作机会方面提出建议方面却鲜为人知。在当前的研究中,我们旨在为企业构建推荐系统,以提供有关业务协作的建议。根据Yelp的历史数据显示,同一用户在同一季节内审核了两家公司,我们能够确定将来可能吸引相同客户的公司,并向他们提供协作建议。我们的结果表明,证据(两家企业共享同一用户的评论)可以为企业寻求未来的协作营销机会提供建议。

著录项

  • 作者

    Tsai Chun-Hua;

  • 作者单位
  • 年度 2016
  • 总页数
  • 原文格式 PDF
  • 正文语种 en
  • 中图分类

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