基于WLAN移动定位的个性化商品信息推荐平台

随着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据，缺乏对室内数据的挖掘分析，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。

Personalized WeChat recommendation system based on indoor WLAN localization

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.