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

• Published in: IEEE transactions on multimedia Vol. 19; no. 8; pp. 1933 - 1945
• Main Authors: Xu, Zhenxing, Chen, Ling, Dai, Yimeng, Chen, Gencai
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Categories; Computational modeling; Data mining; Dynamic topic model (DTM); Factorization; Feature extraction; geotagged photo; Global Positioning System; History; Metadata; Recommender systems; Regularization; Similarity; Sparsity; Trajectory; travel recommendation
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2017.2688928

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.