Point-of-Interest recommendation is an efficient way to explore interesting unknown locations in social media mining of social networks. In order to solve the problem of sparse data and inaccuracy of single user model, we propose a User-City-Sequence Probabilistic Generation Model (UCSPGM) integrating a collective individual self-adaptive Markov model and the topic model. The collective individual self-adaptive Markov model consists of three parts such as the collective Markov model, the individual self-adaptive Markov model and the self-adaptive rank method. The former determines the topic sequence for all users in system and mines the behavioral patterns of users in a large environment. The later mines behavioral patterns for each user in a small environment. The last determines a self-adaptive-rank for each user in niche. We conduct a large amount of experiments to verify the effectiveness and efficiency of our method.

中文翻译：

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社交网络中一种新的自适应混合马尔可夫主题模型 Poi 推荐

兴趣点推荐是一种在社交网络的社交媒体挖掘中探索有趣的未知位置的有效方法。为了解决单用户模型数据稀疏和不准确的问题，我们提出了一种集合个体自适应马尔可夫模型和主题模型相结合的用户-城市-序列概率生成模型（UCSPGM）。集体个体自适应马尔科夫模型由集体马尔科夫模型、个体自适应马尔科夫模型和自适应秩方法三部分组成。前者确定系统中所有用户的主题顺序，挖掘用户在大环境中的行为模式。后者在小环境中为每个用户挖掘行为模式。最后一个为利基中的每个用户确定一个自适应等级。