In today’s social media platforms, when users upload or share their media (photos, videos, bookmarks, etc.), they often annotate it with keywords (called tags). Annotating the media helps in retrieving and browsing resources, and also allows the users to search and browse annotated media. In many social media platforms like Flickr or YouTube, users have to manually annotate their resources, which is inconvenient and time consuming. Tag recommendation is the process of suggesting relevant tags for a given resource, and a tag recommender is a system that recommends the tags. A tag recommender system is important for social media platforms to help users in annotating their resources. Many of the existing tag recommendation methods exploit only the tagging information (Jaschke et al., 2007, Marinho & Schmidt-Thieme, 2008, Sigurbjornsson & van Zwol, 2008). However, many social media platforms support other media features like geographical coordinates. These features can be exploited for improving tag recommendation. In this chapter, a comparison of three types of social media features for tag recommendation is presented and evaluated. The features presented in this chapter include geographical-coordinates, low-level image descriptors, and tags.
Accuracy improvements for cold-start recommendation problem using indirect relations in social networks
