Piloted search and recommendation with social tag cloud-based navigation

We investigate the generation of tag clouds using Bayesian models and test the hypothesis that social network information is better than overall popularity for ranking new and relevant information. We propose three tag cloud generation models based on popularity, topics and social structure. We conducted two user evaluations to compare the models for search and recommendation of music with social network data gathered from ”Last.fm”. Our survey shows that search with tag clouds is not practical whereas recommendation is promising. We report statistical results and compare the performance of the models in generating tag clouds that lead users to discover songs that they liked and were new to them. We find statistically significant evidence at 5% confidence level that the topic and social models outperform the popular model.