(A) location-aware topic recommendation based on social networks

Abstract

Recently, huge amounts of contents have arisen in the society as social network services become popular. Therefore, developing the way for choosing and recommending most appropriate contents to users among diverse contents is a crucial issue. Moreover, a user can easily create and share various contents in their life with a smartphone, a core device for utilizing social networks. One of the most important standards to distinguish proper contents for a user is the user’s location information. The contents of suitable data for the user are different according to the user’s location. Tagging is an effective way to categorize contents for providing appropriate contents to a user based on the user’s location information. However, since creating tags are up to users, this informality of tags cause ambiguous semantics. If we can know the precise semantics of tags, we can provide a precise recommendation for the contents. In this thesis, we propose a new method that recommends topics highly related to a user’s current location by collecting semantic categories of tags. We collect tags attached to the contents which are generated near from the user’s current location and rank them using our scoring function. In addition, we match and categorize the ranked top-K tags to an ontology. Users can obtain the information of the location via the proposed method based on a certain social network service. Experimental results based on over 600,000 Flickr photos and 158,436 distinct tags show that the proposed method effectively provides location-aware tags to a user according to the user’s current location.