A User Similarity Calculation Based on the Location for Social Network Services
A User Similarity Calculation Based on the Location for Social Network Services
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DOI：
10.1007/978-3-642-20149-3_5
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发表时间：
2011-04
影响因子：
2.8
通讯作者：
Min-Joong Lee;C. Chung
中科院分区：
文献类型：
--
作者：
Min-Joong Lee;C. Chung
The online social network services have been growing rapidly over the past few years, and the social network services can easily obtain the locations of users with the recent increasing popularity of the GPS enabled mobile device. In the social network, calculating the similarity between users is an important issue. The user similarity has significant impacts to users, communities and service providers by helping them acquire suitable information effectively.There are numerous factors such as the location, the interest and the gender to calculate the user similarity. The location becomes a very important factor among them, since nowadays the social network services are highly coupled with the mobile device which the user holds all the time. There have been several researches on calculating the user similarity. However, most of them did not consider the location. Even if some methods consider the location, they only consider the physical location of the user which cannot be used for capturing the user’s intention.We propose an effective method to calculate the user similarity using the semantics of the location. By using the semantics of the location, we can capture the user’s intention and interest. Moreover, we can calculate the similarity between different locations using the hierarchical location category. To the best of our knowledge, this is the first research that uses the semantics of the location in order to calculate the user similarity. We evaluate the proposed method with a real-world use case: finding the most similar user of a user. We collected more than 251,000 visited locations over 591 users from foursquare. The experimental results show that the proposed method outperforms a popular existing method calculating the user similarity.
