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基于WLAN移动定位的个性化商品信息推荐平台.

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Title: 基于WLAN移动定位的个性化商品信息推荐平台.
Alternate Title: Personalized WeChat recommendation system based on indoor WLAN localization.
Authors: FENG Jin-hai1 fjhbetter@163.com, YANG Lian-he1 yanglh@tjpu.edu.cn, LIU Jun-fa2 liujunfa@ict.ac.cn, HU Li-sha2 huiisha@ict.ac.cn
Source: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Oct2014, Vol. 36 Issue 10, p1925-1931. 7p.
Abstract (English): With the popularity of WLAN (Wireless Local Area Networks) indoors, mobile devices can easily get real-time access to it, which provides us an unprecedented opportunity to understand individual behavior in everyday life. Recently, mining persons, point of interest and behaviors attracts more attentions. A WeChat recommendation system based on indoor WLAN localization is proposed, which uses users' historical information to obtain users' interest from the overload information. Existing location services usually aim for users, outdoor location data, lack analyzing indoor data through mining, and ignore an amount of semantic information in users, location data. The users, activities are traced by the indoor positioning technology. According to the shops which users visited and the products users saw, users, interest is estimated so as to recommend users for personalized products that may interest them. Based on the above work, we design a personalized product recommendation system based on indoor WLAN localization and the WeChat platform. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): WLAN在室内环境的日益普及, 基于现代的移动设备可以方便实时地获取各种有价值 的WLAN数据, 这对我们识别个体日常生活中的多样化行为提供了前所未有的机会。近年来, 用户的兴 趣点与行为模式挖掘等领域日益引起各界的广泛关注, 设计了一套基于室内定位的推荐系统, 基于用户的 历史访问记录, 实现从过载的信息中识别出用户感兴趣的内容。现有的位置服务通常只针对用户的室外 位置数椐, 缺乏对室内数椐的挖掘分析, 忽略了室内位置数据中蕴含的大量语义信息。利用室内定位技术 获取用户在商场中的活动轨迹, 根据用户去过的店铺和浏览过的商品等历史信息, 估算用户的兴趣爱好并 进而向用户个性化地推荐感兴趣的商品, 基于以上思路设计实现了一套基于室内定位和微信平台的个性 化商品推荐系统. [ABSTRACT FROM AUTHOR]
Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: 基于WLAN移动定位的个性化商品信息推荐平台.
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  Data: Personalized WeChat recommendation system based on indoor WLAN localization.
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  Data: <searchLink fieldCode="AR" term="%22FENG+Jin-hai%22">FENG Jin-hai</searchLink><relatesTo>1</relatesTo><i> fjhbetter@163.com</i><br /><searchLink fieldCode="AR" term="%22YANG+Lian-he%22">YANG Lian-he</searchLink><relatesTo>1</relatesTo><i> yanglh@tjpu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22LIU+Jun-fa%22">LIU Jun-fa</searchLink><relatesTo>2</relatesTo><i> liujunfa@ict.ac.cn</i><br /><searchLink fieldCode="AR" term="%22HU+Li-sha%22">HU Li-sha</searchLink><relatesTo>2</relatesTo><i> huiisha@ict.ac.cn</i>
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– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: With the popularity of WLAN (Wireless Local Area Networks) indoors, mobile devices can easily get real-time access to it, which provides us an unprecedented opportunity to understand individual behavior in everyday life. Recently, mining persons, point of interest and behaviors attracts more attentions. A WeChat recommendation system based on indoor WLAN localization is proposed, which uses users' historical information to obtain users' interest from the overload information. Existing location services usually aim for users, outdoor location data, lack analyzing indoor data through mining, and ignore an amount of semantic information in users, location data. The users, activities are traced by the indoor positioning technology. According to the shops which users visited and the products users saw, users, interest is estimated so as to recommend users for personalized products that may interest them. Based on the above work, we design a personalized product recommendation system based on indoor WLAN localization and the WeChat platform. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Chinese)
  Group: Ab
  Data: WLAN在室内环境的日益普及, 基于现代的移动设备可以方便实时地获取各种有价值 的WLAN数据, 这对我们识别个体日常生活中的多样化行为提供了前所未有的机会。近年来, 用户的兴 趣点与行为模式挖掘等领域日益引起各界的广泛关注, 设计了一套基于室内定位的推荐系统, 基于用户的 历史访问记录, 实现从过载的信息中识别出用户感兴趣的内容。现有的位置服务通常只针对用户的室外 位置数椐, 缺乏对室内数椐的挖掘分析, 忽略了室内位置数据中蕴含的大量语义信息。利用室内定位技术 获取用户在商场中的活动轨迹, 根据用户去过的店铺和浏览过的商品等历史信息, 估算用户的兴趣爱好并 进而向用户个性化地推荐感兴趣的商品, 基于以上思路设计实现了一套基于室内定位和微信平台的个性 化商品推荐系统. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3969/j.issn.1007-130X.2014.10.014
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      – Code: chi
        Text: Chinese
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        PageCount: 7
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      – TitleFull: 基于WLAN移动定位的个性化商品信息推荐平台.
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            NameFull: FENG Jin-hai
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            NameFull: YANG Lian-he
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            NameFull: LIU Jun-fa
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            NameFull: HU Li-sha
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              M: 10
              Text: Oct2014
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