Exploiting Visual Content for Travel Location Recommendation

Image has become an important source to improve the quality of travel location recommendation, it reflects user interests and travel location properties. However, little work exists for travel location recommendation by exploiting images. In this study, we propose a method for accurate and personalized travel location recommendations using visual content. Specifically, a convolutional neural network is used to extract visual content which is used to learn the latent feature representation of implicit feedback and uncover user interests and travel location properties. In addition, the third-party web service is used to extract age and gender features from the images to understanding user interest and the travel location properties. Experimental results on real-world datasets demonstrate the effectiveness of our method.