Simultaneous image clustering, classification and annotation for tourism recommendation

Abstract

The exponential increase in the amount of data uploaded to the web has led to a surge of interest in multimedia recommendation and annotation. Due to the vast volume of data, efficient algorithms for recommendation and annotation are needed. Here, a novel two-step approach is proposed, which annotates an image received as input and recommends several tourist destinations strongly related to the image. It is based on probabilistic latent semantic analysis and hypergraph ranking enhanced with the visual attributes of the images. The proposed method is tested on a dataset of 30000 images bearing text information (e.g., title, tags) collected from Flickr. The experimental results are very promising, as they achieve a top rank precision of 80% for tourism recommendation. © 2014 Springer International Publishing.