In the online-to-offline (O2O) business model, location recommendation plays an important role and is an essential component of the location-based services. The check-in data, which contains both the geographical and temporal information, has been treated as an important data source for location recommendation. Location-based collaborative filtering is a popular technique for computing location similarities to arrive at the recommendation. In this research we analyze the geographical and temporal characteristics of the user’s check-in activity and incorporate it for deriving recommendations using location-based collaborative filtering. To model the geographical proximity between the recommended location and the visited location, we first get the user’s active regions using the multiple-center discovering algorithm; we then derive the probability of visiting the unvisited locations by using the power-law distribution on the distance. The geographical proximity is derived by multiplying the visiting probability and the check-in ratio of the active region. To consider temporal information, we propose the concept of time-aware location similarity, which splits the user check-ins into twenty-four different time slots in a day. To address the sparsity problem created by splitting check-in data, we propose a mechanism to measure the similarities between time slots and use these similarities to infer the empty ratings. The geographical proximity and time-aware location similarity are integrated to generate the location similarity. We perform the experiments to verify the effectiveness of the proposed algorithm. The experimental results show the superiority of our method compared with the benchmarks.

中文翻译：

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将地理和时间影响整合到位置推荐中：一种基于签到的方法

在在线到离线（O2O）业务模型中，位置推荐扮演着重要角色，并且是基于位置的服务的重要组成部分。包含地理和时间信息的签到数据已被视为位置推荐的重要数据源。基于位置的协作过滤是一种流行的技术，用于计算位置相似度以获得推荐。在这项研究中，我们分析了用户签到活动的地理和时间特征，并将其结合起来以使用基于位置的协作过滤来推导推荐。为了对推荐位置和访问位置之间的地理邻近度进行建模，我们首先使用多中心发现算法获得用户的活动区域；然后，我们使用距离上的幂律分布来得出访问未访问位置的概率。地理邻近度是通过将访问概率与活动区域的签到率相乘得出的。为了考虑时间信息，我们提出了时间感知位置相似性的概念，该概念将用户签到分成一天中的24个不同的时隙。为了解决因分割签入数据而造成的稀疏性问题，我们提出了一种机制，可以测量时隙之间的相似度，并使用这些相似度来推断空等级。地理邻近度和时间感知位置相似度被集成以生成位置相似度。我们进行实验以验证所提出算法的有效性。