Location recommendation methods suggest unvisited locations to their users. Many existing location recommendation methods focus on the spatial, social and temporal aspects of human movements. However, contextual information is also invaluable to location recommendation methods and has the great potential for explaining what triggers users to show different behaviors. CLR learns the response of the users to contextual variables based on their own history and the history of similar behaving users. In this paper, we propose a contextual location recommendation method named Contextual Location Recommendation (CLR) that learns the intention and spatial responses of users to various contextual triggers using the historical check-in and contextual information. CLR starts with a co-variance analysis to reduce dimensionality of the check-in data and then uses an optimized version of the random walk with restart to extract hidden user responses to contextual triggers. A tensor factorization is used to build a latent-factor model to predict the user’s intention response with the given set of contextual triggers. Based on the intention response of the user, a contextual spatial component identifies a set of matching locations accessible to the user by estimating the probability distribution of the location of the user and the popularity probability of locations under the contextual settings. Experimental results on three real-world datasets show that CLR improves the recommendation precision by 35% compared to the best-performing baseline recommendation method.

位置推荐方法向用户建议未访问的位置。许多现有的位置推荐方法侧重于人类运动的空间、社会和时间方面。然而，上下文信息对于位置推荐方法也是非常宝贵的，并且在解释是什么触发用户表现出不同的行为方面具有很大的潜力。CLR 根据用户自己的历史和行为相似的用户的历史，了解用户对上下文变量的反应。在本文中，我们提出了一种名为 Contextual Location Recommendation (CLR) 的上下文位置推荐方法，该方法使用历史签到和上下文信息来学习用户对各种上下文触发器的意图和空间响应。CLR 从协方差分析开始，以降低签入数据的维度，然后使用优化版本的随机游走和重启来提取隐藏的用户对上下文触发器的响应。张量分解用于构建潜在因子模型，以使用给定的上下文触发器集预测用户的意图响应。基于用户的意图响应，上下文空间组件通过估计用户位置的概率分布和上下文设置下位置的流行概率来识别用户可访问的一组匹配位置。在三个真实世界数据集上的实验结果表明，与性能最佳的基线推荐方法相比，CLR 将推荐精度提高了 35%。