Abstract
Industry and researchers have identified numerous ways to monetize microblogs for personalization and recommenda- Tion. A common challenge across these different works is the identification of user interests. Although techniques have been developed to address this challenge, a exible approach that spans multiple levels of granularity in user interests has not been forthcoming. In this work, we focus on exploiting hierarchical semantics of concepts to infer richer user inter- ests expressed as a Hierarchical Interest Graph. To create such graphs, we utilize users' tweets to first ground potential user interests to structured background knowledge such as Wikipedia Category Graph. We then adapt spreading acti- vation theory to assign user interest score to each category in the hierarchy. The Hierarchical Interest Graph not only comprises of users' explicitly mentioned interests determined from Twitter, but also their implicit interest categories in- ferred from the background knowledge source.