On Understanding User Interests through Heterogeneous Data Sources

User interests can be learned from multiple sources, each of them presenting only partial facets. We propose an approach to merge user information from disparate data sources to enable a more complete, enriched view of user interests. Using our approach, we show that merging different sources results in three times of more interest categories in user profiles than with each single source and that merged profiles can capture much more common interests among a group of users, which is key to group profiling.