Location recommendation for location-based social networks
Location recommendation for location-based social networks
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DOI：
10.1145/1869790.1869861
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发表时间：
2010-11
期刊：
影响因子：
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通讯作者：
Mao Ye;Peifeng Yin;Wang-Chien Lee
中科院分区：
文献类型：
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作者：
Mao Ye;Peifeng Yin;Wang-Chien Lee
在本文中，我们研究了在大规模的基于位置的社交网络的位置推荐服务的研究问题，通过利用用户和位置/地点的社会和地理特征。通过我们对Foursquare（一个流行的基于位置的社交网络系统）收集的数据集的分析，我们观察到系统中用户和他们最喜欢的位置/地点之间存在很强的社交和地理空间联系。因此，我们开发了一个基于朋友的协同过滤（FCF）方法的位置推荐的基础上，由社会朋友的地方的协作评级。此外，我们基于从Foursquare数据集中观察到的地理空间特征中推导出的启发式方法，提出了FCF技术的一种变体，即地理测量FCF（GM-FCF）。最后，评估结果表明，所提出的家庭的FCF技术具有可比的推荐效果对国家的最先进的推荐算法，而产生显着降低计算开销。同时，GM-FCF在推荐有效性和计算开销之间的权衡中提供了额外的灵活性。
In this paper, we study the research issues in realizing location recommendation services for large-scale location-based social networks, by exploiting the social and geographical characteristics of users and locations/places. Through our analysis on a dataset collected from Foursquare, a popular location-based social networking system, we observe that there exists strong social and geospatial ties among users and their favorite locations/places in the system. Accordingly, we develop a friend-based collaborative filtering (FCF) approach for location recommendation based on collaborative ratings of places made by social friends. Moreover, we propose a variant of FCF technique, namely Geo-Measured FCF (GM-FCF), based on heuristics derived from observed geospatial characteristics in the Foursquare dataset. Finally, the evaluation results show that the proposed family of FCF techniques holds comparable recommendation effectiveness against the state-of-the-art recommendation algorithms, while incurring significantly lower computational overhead. Meanwhile, the GM-FCF provides additional flexibility in tradeoff between recommendation effectiveness and computational overhead.