Image Ranking Based on User Browsing Behavior

Ranking of images is difficult because many factors determine their importance (e.g., popularity, quality, entertainment value, context, etc.). In social media platforms, ranking also depends on social interactions and on the visibility of the images both inside and outside those platforms. In this context, the application of standard ranking methods is not clearly understood, and neither are the subtleties associated with taking into account social interaction, internal, and external factors. In this paper, we use a large Flickr dataset and investigate these factors by performing an in-depth analysis of several ranking algorithms using both internal (i.e., within Flickr) and external (i.e., links from outside of Flickr) factors. We analyze rankings given by common metrics used in image retrieval (e.g., number of favorites), and compare them with metrics based on page views (e.g., time spent, number of views). In addition, we represent users' navigation by a graph and combine session models with some of these metrics, comparing with PageRank and BrowseRank. Our experiments show significant differences between the rankings, providing insights on the impact of social interactions, internal, and external factors in image ranking.