An Outdoor Recommendation System based on User Location History
An Outdoor Recommendation System based on User Location History
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DOI：
10.1007/11833529_64
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发表时间：
2005-03
期刊：
影响因子：
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通讯作者：
Yuichiro Takeuchi;Masanori Sugimoto
中科院分区：
文献类型：
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作者：
Yuichiro Takeuchi;Masanori Sugimoto
推荐系统能够自动了解用户偏好并做出推荐，目前广泛应用于在线购物中。然而，到目前为止，还很少有人尝试将它们应用到现实世界的购物中。在本文中，我们提出了一种新颖的现实世界推荐系统，该系统根据用户过去的位置数据历史记录来推荐商店。该系统使用新设计的地点学习算法，可以有效地找到用户经常光顾的地点，并附上他们的专有名称（例如“上野皇家博物馆”）。用户经常光顾的商店被用作基于项目的协同过滤算法的输入以进行推荐。此外，我们还提供了一种基于用户移动和城市地理条件的预测来进一步缩小商店范围的方法。我们在东京的一个热门购物区评估了我们的系统，结果证明了我们整体方法的有效性。
Recommendation systems, which automatically understand user preferences and make recommendations, are now widely used in online shopping. However, so far there have been few attempts of applying them to real-world shopping. In this paper, we propose a novel real-world recommendation system, which makes recommendations of shops based on users’ past location data history. The system uses a newly devised place learning algorithm, which can efficiently find users’ frequented places, complete with their proper names (e.g. “The Ueno Royal Museum”). Users’ frequented shops are used as input to the item-based collaborative filtering algorithm to make recommendations. In addition, we provide a method for further narrowing down shops based on prediction of user movement and geographical conditions of the city. We have evaluated our system at a popular shopping district inside Tokyo, and the results demonstrate the effectiveness of our overall approach.