基于位置社交网络中时间地点感知的POI推荐
Time and Location Aware Points of Interest Recommendation in Location-Based Social Networks
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摘要: 基于位置社交网络的飞速发展带来了大量的check-in数据，使得POI兴趣点推荐成为可能。近年来，分布式表示致力于通过学习低维稠密向量减轻数据的稀疏问题。现有用于POI的表示学习将用户和POI嵌入到同一向量空间。该类方法违背了用户和POI的语义，因为其本质上属于不同的对象。本文提出一种基于翻译模型、时空感知的（TransTL）表示，将时空信息视为连接用户和POI的关系。本文模型属于知识图表示的推广。基本思想为将一个Abstract: The wide spread of location-based social networks brings about a huge volume of user check-in data, which facilitates the recommendation of points of interest (POIs). Recent advances on distributed representation shed light on learning low dimensional dense vectors to alleviate the data sparsity problem. Current studies on representation learning for POI recommendation embed both users and POIs in a common latent space, and users' preference is inferred based on the distance/similarity between a user and a POI. Such an approach is not in accordance with the semantics of users and POIs as they are inherently different objects. In this paper, we present a novel translation-based, time and location aware (TransTL) representation, which models the spatial and temporal information as a relationship connecting users and POIs. Our model generalizes the recent advances in knowledge graph embedding. The basic idea is that the embedding of a
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