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A Dynamic Topic Model and Matrix Factorization-Based Travel Recommendation Method Exploiting Ubiquitous Data

The vast volumes of community-contributed geotagged photos (CCGPs) available on the Web can be utilized to make travel location recommendations. The sparsity of user location interactions makes it difficult to learn travel preferences, because a user usually visits only a limited number of travel locations. Static topic models can be used to solve the sparsity problem by considering user travel topics. However, all travel histories of a user are regarded as one document drawn from a set of static topics, ignoring the evolving of topics and travel preferences. In this paper, we propose a dynamic topic model (DTM) and matrix factorization (MF)-based travel recommendation method. A DTM is used to obtain the temporally fine-grained topic distributions (i.e., implicit topic information) of users and locations. In addition, a large amount of explicit information is extracted from the metadata and visual contents of CCGPs, check-ins, and point of interest categories datasets. The information is used to obtain user–user and location–location similarity information, which is imposed as two regularization terms to constraint MF. The proposed method is evaluated on a publicly available Flickr dataset. Experimental results demonstrate that the proposed method can generate significantly superior recommendations compared to other state-of-the-art travel location recommendation studies.