基于位置社交网络中的传播概率研究
首发时间:2018-01-24
摘要:基于位置的社交网络(LocationBasedSocial Network,LBSN)近年来受到越来越多用户的喜爱。LBSN中不仅包含了用户的线上社交信息,同时包含了线下签到活动信息。借助用户在网络上的签到信息,可以帮助推广、宣传商家,从而为商家节约广告成本。现有研究基于影响力最大化算法帮助商家推广位置。但是,在设计影响力最大化算法时,如何挖掘LBSN中有价值的信息,更准确地度量用户间的传播概率成为一个挑战。因此,本文提出一种度量传播概率的方法--基于用户访问概率的传播概率预测模型(PropagationProbabilitybasedonUserAcessProbability, PPUAP)。结合用户兴趣、流动性和地点吸引力三方面特征度量传播概率。通过在真实数据集Yelp上进行评估,发现PPUAP的准确率高于基于流动性的传播概率预测模型。
关键词: 传播概率 位置推广 影响力最大化 基于位置的社交网络
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Propagation Probability in Location Based Social Networks
Abstract:Location Based Social Network (LBSN) has been favored by more and more users in recent years. LBSN not only includes the user\'s online social information, but also includes offline check-in information. With user check-in information online, helping businesses easly promote and propagate their locations, thus saving their advertisers costs. Existing research helps merchants promote their locations based on influence maximization algorithms. However, how to mine valuable information in LBSN and measure the propagation probability more accurately becomes a challenge in the design of influence maximization algorithm. Therefore, this paper proposes a model to measure propagation probability, that is,Propagation Probability based on User Acess Probability (PPUAP). Combining the three aspects of user interest, mobility and location attraction to measure the propagation probability. By evaluating on the real dataset Yelp, PPUAP was found to be more accurate than the mobility based propagationprobability prediction model.
Keywords: Propagation Probability Location Promotion Influence Maximization Location Based Social Network
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