Venue Suggestion Using Social-Centric Scores
This work addresses venue recommendation for users on social platforms, but it is incremental as it builds on existing user modeling techniques with a focus on social data.
The paper tackled the problem of personalized venue suggestion by introducing social-centric scores derived from location-based social networks, and found that these scores outperformed content-based scores in experiments on the TREC dataset.
User modeling is a very important task for making relevant suggestions of venues to the users. These suggestions are often based on matching the venues' features with the users' preferences, which can be collected from previously visited locations. In this paper, we present a set of relevance scores for making personalized suggestions of points of interest. These scores model each user by focusing on the different types of information extracted from venues that they have previously visited. In particular, we focus on scores extracted from social information available on location-based social networks. Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, show that social scores are more effective than scores based venues' content.